Project studio for finance proof

Act as a junior Investment Banking analyst. Use public information to produce a decision-ready work product for a manager or interview panel.

Show that you can apply Accounting fluency, Financial modeling, Valuation, M&A analysis, Credit analysis in a practical analyst workflow, not only explain the theory.

• Pick one listed company and define the valuation date.
• Collect financial statements for at least three years.
• Choose 4-6 comparable companies and capture market multiples.
• Create an assumptions tab with revenue, margin, capex, working capital, WACC, and terminal growth.

1. Build historical financials and clean one-off items.
2. Forecast revenue, costs, taxes, capex, and working capital.
3. Calculate DCF value, trading comps value, and transaction comps value where possible.
4. Run sensitivity on WACC, terminal growth, exit multiple, and margin assumptions.
5. Write a final recommendation explaining upside/downside, key risks, and what would change your view.

• Professional brief
• Financial model or analytical workbook
• Presentation deck
• Source log
• Interview talking points
• Source and assumption log
• One-page executive summary
• Final output file

• Annual reports, investor presentations, exchange filings, SEBI/RBI/NSE/BSE pages, and official company disclosures.
• Latest annual report
• Quarterly results or investor presentation
• NSE/BSE price history
• Peer company filings
• Public transaction/news sources

• Clear problem statement and role context
• Source-backed assumptions
• Clean model, memo, notebook, or dashboard
• Decision recommendation with risks
• Resume bullet and interview talk track

• Problem: explain the business question and why it matters for Investment Banking.
• Method: describe the data collected, assumptions made, and analysis performed.
• Decision: state the recommendation, key risk, and what would change your view.

Act as a junior Investment Banking analyst. Use public information to produce a decision-ready work product for a manager or interview panel.

Show that you can apply Accounting fluency, Financial modeling, Valuation, M&A analysis, Credit analysis in a practical analyst workflow, not only explain the theory.

• Pick one listed company and define the valuation date.
• Collect financial statements for at least three years.
• Choose 4-6 comparable companies and capture market multiples.
• Create an assumptions tab with revenue, margin, capex, working capital, WACC, and terminal growth.

1. Build historical financials and clean one-off items.
2. Forecast revenue, costs, taxes, capex, and working capital.
3. Calculate DCF value, trading comps value, and transaction comps value where possible.
4. Run sensitivity on WACC, terminal growth, exit multiple, and margin assumptions.
5. Write a final recommendation explaining upside/downside, key risks, and what would change your view.

• Professional brief
• Financial model or analytical workbook
• Presentation deck
• Source log
• Interview talking points
• Source and assumption log
• One-page executive summary
• Final output file

• Public transaction announcements, DRHP/RHP filings, exchange releases, rating reports, and company presentations.
• Latest annual report
• Quarterly results or investor presentation
• NSE/BSE price history
• Peer company filings
• Public transaction/news sources

• Clear problem statement and role context
• Source-backed assumptions
• Clean model, memo, notebook, or dashboard
• Decision recommendation with risks
• Resume bullet and interview talk track

• Problem: explain the business question and why it matters for Investment Banking.
• Method: describe the data collected, assumptions made, and analysis performed.
• Decision: state the recommendation, key risk, and what would change your view.

Act as a junior Investment Banking analyst. Use public information to produce a decision-ready work product for a manager or interview panel.

Show that you can apply Accounting fluency, Financial modeling, Valuation, M&A analysis, Credit analysis in a practical analyst workflow, not only explain the theory.

• Pick one listed company and define the valuation date.
• Collect financial statements for at least three years.
• Choose 4-6 comparable companies and capture market multiples.
• Create an assumptions tab with revenue, margin, capex, working capital, WACC, and terminal growth.

1. Build historical financials and clean one-off items.
2. Forecast revenue, costs, taxes, capex, and working capital.
3. Calculate DCF value, trading comps value, and transaction comps value where possible.
4. Run sensitivity on WACC, terminal growth, exit multiple, and margin assumptions.
5. Write a final recommendation explaining upside/downside, key risks, and what would change your view.

• Professional brief
• Financial model or analytical workbook
• Presentation deck
• Source log
• Interview talking points
• Source and assumption log
• One-page executive summary
• Final output file

• Public filings, official regulatory material, public datasets, company career/domain research, and a documented source log.
• Latest annual report
• Quarterly results or investor presentation
• NSE/BSE price history
• Peer company filings
• Public transaction/news sources

• Clear problem statement and role context
• Source-backed assumptions
• Clean model, memo, notebook, or dashboard
• Decision recommendation with risks
• Resume bullet and interview talk track

• Problem: explain the business question and why it matters for Investment Banking.
• Method: describe the data collected, assumptions made, and analysis performed.
• Decision: state the recommendation, key risk, and what would change your view.

Act as a junior Investment Banking analyst. Use public information to produce a decision-ready work product for a manager or interview panel.

Show that you can apply Accounting fluency, Financial modeling, Valuation, M&A analysis, Credit analysis in a practical analyst workflow, not only explain the theory.

• Pick one listed company and define the valuation date.
• Collect financial statements for at least three years.
• Choose 4-6 comparable companies and capture market multiples.
• Create an assumptions tab with revenue, margin, capex, working capital, WACC, and terminal growth.

1. Build historical financials and clean one-off items.
2. Forecast revenue, costs, taxes, capex, and working capital.
3. Calculate DCF value, trading comps value, and transaction comps value where possible.
4. Run sensitivity on WACC, terminal growth, exit multiple, and margin assumptions.
5. Write a final recommendation explaining upside/downside, key risks, and what would change your view.

• Professional brief
• Financial model or analytical workbook
• Presentation deck
• Source log
• Interview talking points
• Source and assumption log
• One-page executive summary
• Final output file

• Public filings, official regulatory material, public datasets, company career/domain research, and a documented source log.
• Latest annual report
• Quarterly results or investor presentation
• NSE/BSE price history
• Peer company filings
• Public transaction/news sources

• Clear problem statement and role context
• Source-backed assumptions
• Clean model, memo, notebook, or dashboard
• Decision recommendation with risks
• Resume bullet and interview talk track

• Problem: explain the business question and why it matters for Investment Banking.
• Method: describe the data collected, assumptions made, and analysis performed.
• Decision: state the recommendation, key risk, and what would change your view.

Act as a junior Investment Banking analyst. Use public information to produce a decision-ready work product for a manager or interview panel.

Show that you can apply Accounting fluency, Financial modeling, Valuation, M&A analysis, Credit analysis in a practical analyst workflow, not only explain the theory.

• Pick one listed company and define the valuation date.
• Collect financial statements for at least three years.
• Choose 4-6 comparable companies and capture market multiples.
• Create an assumptions tab with revenue, margin, capex, working capital, WACC, and terminal growth.

1. Build historical financials and clean one-off items.
2. Forecast revenue, costs, taxes, capex, and working capital.
3. Calculate DCF value, trading comps value, and transaction comps value where possible.
4. Run sensitivity on WACC, terminal growth, exit multiple, and margin assumptions.
5. Write a final recommendation explaining upside/downside, key risks, and what would change your view.

• Professional brief
• Financial model or analytical workbook
• Presentation deck
• Source log
• Interview talking points
• Source and assumption log
• One-page executive summary
• Final output file

• Public filings, official regulatory material, public datasets, company career/domain research, and a documented source log.
• Latest annual report
• Quarterly results or investor presentation
• NSE/BSE price history
• Peer company filings
• Public transaction/news sources

• Clear problem statement and role context
• Source-backed assumptions
• Clean model, memo, notebook, or dashboard
• Decision recommendation with risks
• Resume bullet and interview talk track

• Problem: explain the business question and why it matters for Investment Banking.
• Method: describe the data collected, assumptions made, and analysis performed.
• Decision: state the recommendation, key risk, and what would change your view.

Act as a junior Private Equity analyst. Use public information to produce a decision-ready work product for a manager or interview panel.

Show that you can apply Financial modeling, Valuation, LBO modeling, Due diligence, Investment memo writing in a practical analyst workflow, not only explain the theory.

• Pick one listed company and define the valuation date.
• Collect financial statements for at least three years.
• Choose 4-6 comparable companies and capture market multiples.
• Create an assumptions tab with revenue, margin, capex, working capital, WACC, and terminal growth.

1. Build historical financials and clean one-off items.
2. Forecast revenue, costs, taxes, capex, and working capital.
3. Calculate DCF value, trading comps value, and transaction comps value where possible.
4. Run sensitivity on WACC, terminal growth, exit multiple, and margin assumptions.
5. Write a final recommendation explaining upside/downside, key risks, and what would change your view.

• Professional brief
• Financial model or analytical workbook
• Presentation deck
• Source log
• Interview talking points
• Source and assumption log
• One-page executive summary
• Final output file

• Annual reports, investor presentations, exchange filings, SEBI/RBI/NSE/BSE pages, and official company disclosures.
• Latest annual report
• Quarterly results or investor presentation
• NSE/BSE price history
• Peer company filings
• Public transaction/news sources

• Clear problem statement and role context
• Source-backed assumptions
• Clean model, memo, notebook, or dashboard
• Decision recommendation with risks
• Resume bullet and interview talk track

• Problem: explain the business question and why it matters for Private Equity.
• Method: describe the data collected, assumptions made, and analysis performed.
• Decision: state the recommendation, key risk, and what would change your view.

Act as a junior Private Equity analyst. Use public information to produce a decision-ready work product for a manager or interview panel.

Show that you can apply Financial modeling, Valuation, LBO modeling, Due diligence, Investment memo writing in a practical analyst workflow, not only explain the theory.

• Pick one listed company and define the valuation date.
• Collect financial statements for at least three years.
• Choose 4-6 comparable companies and capture market multiples.
• Create an assumptions tab with revenue, margin, capex, working capital, WACC, and terminal growth.

1. Build historical financials and clean one-off items.
2. Forecast revenue, costs, taxes, capex, and working capital.
3. Calculate DCF value, trading comps value, and transaction comps value where possible.
4. Run sensitivity on WACC, terminal growth, exit multiple, and margin assumptions.
5. Write a final recommendation explaining upside/downside, key risks, and what would change your view.

• Professional brief
• Financial model or analytical workbook
• Presentation deck
• Source log
• Interview talking points
• Source and assumption log
• One-page executive summary
• Final output file

• Public transaction announcements, DRHP/RHP filings, exchange releases, rating reports, and company presentations.
• Latest annual report
• Quarterly results or investor presentation
• NSE/BSE price history
• Peer company filings
• Public transaction/news sources

• Clear problem statement and role context
• Source-backed assumptions
• Clean model, memo, notebook, or dashboard
• Decision recommendation with risks
• Resume bullet and interview talk track

• Problem: explain the business question and why it matters for Private Equity.
• Method: describe the data collected, assumptions made, and analysis performed.
• Decision: state the recommendation, key risk, and what would change your view.

Act as a junior Private Equity analyst. Use public information to produce a decision-ready work product for a manager or interview panel.

Show that you can apply Financial modeling, Valuation, LBO modeling, Due diligence, Investment memo writing in a practical analyst workflow, not only explain the theory.

• Pick one listed company and define the valuation date.
• Collect financial statements for at least three years.
• Choose 4-6 comparable companies and capture market multiples.
• Create an assumptions tab with revenue, margin, capex, working capital, WACC, and terminal growth.

1. Build historical financials and clean one-off items.
2. Forecast revenue, costs, taxes, capex, and working capital.
3. Calculate DCF value, trading comps value, and transaction comps value where possible.
4. Run sensitivity on WACC, terminal growth, exit multiple, and margin assumptions.
5. Write a final recommendation explaining upside/downside, key risks, and what would change your view.

• Professional brief
• Financial model or analytical workbook
• Presentation deck
• Source log
• Interview talking points
• Source and assumption log
• One-page executive summary
• Final output file

• Public filings, official regulatory material, public datasets, company career/domain research, and a documented source log.
• Latest annual report
• Quarterly results or investor presentation
• NSE/BSE price history
• Peer company filings
• Public transaction/news sources

• Clear problem statement and role context
• Source-backed assumptions
• Clean model, memo, notebook, or dashboard
• Decision recommendation with risks
• Resume bullet and interview talk track

• Problem: explain the business question and why it matters for Private Equity.
• Method: describe the data collected, assumptions made, and analysis performed.
• Decision: state the recommendation, key risk, and what would change your view.

Act as a junior Private Equity analyst. Use public information to produce a decision-ready work product for a manager or interview panel.

Show that you can apply Financial modeling, Valuation, LBO modeling, Due diligence, Investment memo writing in a practical analyst workflow, not only explain the theory.

• Pick one listed company and define the valuation date.
• Collect financial statements for at least three years.
• Choose 4-6 comparable companies and capture market multiples.
• Create an assumptions tab with revenue, margin, capex, working capital, WACC, and terminal growth.

1. Build historical financials and clean one-off items.
2. Forecast revenue, costs, taxes, capex, and working capital.
3. Calculate DCF value, trading comps value, and transaction comps value where possible.
4. Run sensitivity on WACC, terminal growth, exit multiple, and margin assumptions.
5. Write a final recommendation explaining upside/downside, key risks, and what would change your view.

• Professional brief
• Financial model or analytical workbook
• Presentation deck
• Source log
• Interview talking points
• Source and assumption log
• One-page executive summary
• Final output file

• Public filings, official regulatory material, public datasets, company career/domain research, and a documented source log.
• Latest annual report
• Quarterly results or investor presentation
• NSE/BSE price history
• Peer company filings
• Public transaction/news sources

• Clear problem statement and role context
• Source-backed assumptions
• Clean model, memo, notebook, or dashboard
• Decision recommendation with risks
• Resume bullet and interview talk track

• Problem: explain the business question and why it matters for Private Equity.
• Method: describe the data collected, assumptions made, and analysis performed.
• Decision: state the recommendation, key risk, and what would change your view.

Act as a junior Private Equity analyst. Use public information to produce a decision-ready work product for a manager or interview panel.

Show that you can apply Financial modeling, Valuation, LBO modeling, Due diligence, Investment memo writing in a practical analyst workflow, not only explain the theory.

• Pick one listed company and define the valuation date.
• Collect financial statements for at least three years.
• Choose 4-6 comparable companies and capture market multiples.
• Create an assumptions tab with revenue, margin, capex, working capital, WACC, and terminal growth.

1. Build historical financials and clean one-off items.
2. Forecast revenue, costs, taxes, capex, and working capital.
3. Calculate DCF value, trading comps value, and transaction comps value where possible.
4. Run sensitivity on WACC, terminal growth, exit multiple, and margin assumptions.
5. Write a final recommendation explaining upside/downside, key risks, and what would change your view.

• Professional brief
• Financial model or analytical workbook
• Presentation deck
• Source log
• Interview talking points
• Source and assumption log
• One-page executive summary
• Final output file

• Public filings, official regulatory material, public datasets, company career/domain research, and a documented source log.
• Latest annual report
• Quarterly results or investor presentation
• NSE/BSE price history
• Peer company filings
• Public transaction/news sources

• Clear problem statement and role context
• Source-backed assumptions
• Clean model, memo, notebook, or dashboard
• Decision recommendation with risks
• Resume bullet and interview talk track

• Problem: explain the business question and why it matters for Private Equity.
• Method: describe the data collected, assumptions made, and analysis performed.
• Decision: state the recommendation, key risk, and what would change your view.

Act as a junior Venture Capital analyst. Use public information to produce a decision-ready work product for a manager or interview panel.

Show that you can apply Deal sourcing, Unit economics, Fundraising and cap tables, Investment memo writing, Finance product management in a practical analyst workflow, not only explain the theory.

• Pick one listed company and define the valuation date.
• Collect financial statements for at least three years.
• Choose 4-6 comparable companies and capture market multiples.
• Create an assumptions tab with revenue, margin, capex, working capital, WACC, and terminal growth.

1. Build historical financials and clean one-off items.
2. Forecast revenue, costs, taxes, capex, and working capital.
3. Calculate DCF value, trading comps value, and transaction comps value where possible.
4. Run sensitivity on WACC, terminal growth, exit multiple, and margin assumptions.
5. Write a final recommendation explaining upside/downside, key risks, and what would change your view.

• Professional brief
• Financial model or analytical workbook
• Presentation deck
• Source log
• Interview talking points
• Source and assumption log
• One-page executive summary
• Final output file

• Annual reports, investor presentations, exchange filings, SEBI/RBI/NSE/BSE pages, and official company disclosures.
• Latest annual report
• Quarterly results or investor presentation
• NSE/BSE price history
• Peer company filings
• Public transaction/news sources

• Clear problem statement and role context
• Source-backed assumptions
• Clean model, memo, notebook, or dashboard
• Decision recommendation with risks
• Resume bullet and interview talk track

• Problem: explain the business question and why it matters for Venture Capital.
• Method: describe the data collected, assumptions made, and analysis performed.
• Decision: state the recommendation, key risk, and what would change your view.

Act as a junior Venture Capital analyst. Use public information to produce a decision-ready work product for a manager or interview panel.

Show that you can apply Deal sourcing, Unit economics, Fundraising and cap tables, Investment memo writing, Finance product management in a practical analyst workflow, not only explain the theory.

• Pick one listed company and define the valuation date.
• Collect financial statements for at least three years.
• Choose 4-6 comparable companies and capture market multiples.
• Create an assumptions tab with revenue, margin, capex, working capital, WACC, and terminal growth.

1. Build historical financials and clean one-off items.
2. Forecast revenue, costs, taxes, capex, and working capital.
3. Calculate DCF value, trading comps value, and transaction comps value where possible.
4. Run sensitivity on WACC, terminal growth, exit multiple, and margin assumptions.
5. Write a final recommendation explaining upside/downside, key risks, and what would change your view.

• Professional brief
• Financial model or analytical workbook
• Presentation deck
• Source log
• Interview talking points
• Source and assumption log
• One-page executive summary
• Final output file

• Public transaction announcements, DRHP/RHP filings, exchange releases, rating reports, and company presentations.
• Latest annual report
• Quarterly results or investor presentation
• NSE/BSE price history
• Peer company filings
• Public transaction/news sources

• Clear problem statement and role context
• Source-backed assumptions
• Clean model, memo, notebook, or dashboard
• Decision recommendation with risks
• Resume bullet and interview talk track

• Problem: explain the business question and why it matters for Venture Capital.
• Method: describe the data collected, assumptions made, and analysis performed.
• Decision: state the recommendation, key risk, and what would change your view.

Act as a junior Venture Capital analyst. Use public information to produce a decision-ready work product for a manager or interview panel.

Show that you can apply Deal sourcing, Unit economics, Fundraising and cap tables, Investment memo writing, Finance product management in a practical analyst workflow, not only explain the theory.

• Pick one listed company and define the valuation date.
• Collect financial statements for at least three years.
• Choose 4-6 comparable companies and capture market multiples.
• Create an assumptions tab with revenue, margin, capex, working capital, WACC, and terminal growth.

1. Build historical financials and clean one-off items.
2. Forecast revenue, costs, taxes, capex, and working capital.
3. Calculate DCF value, trading comps value, and transaction comps value where possible.
4. Run sensitivity on WACC, terminal growth, exit multiple, and margin assumptions.
5. Write a final recommendation explaining upside/downside, key risks, and what would change your view.

• Professional brief
• Financial model or analytical workbook
• Presentation deck
• Source log
• Interview talking points
• Source and assumption log
• One-page executive summary
• Final output file

• Public filings, official regulatory material, public datasets, company career/domain research, and a documented source log.
• Latest annual report
• Quarterly results or investor presentation
• NSE/BSE price history
• Peer company filings
• Public transaction/news sources

• Clear problem statement and role context
• Source-backed assumptions
• Clean model, memo, notebook, or dashboard
• Decision recommendation with risks
• Resume bullet and interview talk track

• Problem: explain the business question and why it matters for Venture Capital.
• Method: describe the data collected, assumptions made, and analysis performed.
• Decision: state the recommendation, key risk, and what would change your view.

Act as a junior Venture Capital analyst. Use public information to produce a decision-ready work product for a manager or interview panel.

Show that you can apply Deal sourcing, Unit economics, Fundraising and cap tables, Investment memo writing, Finance product management in a practical analyst workflow, not only explain the theory.

• Pick one listed company and define the valuation date.
• Collect financial statements for at least three years.
• Choose 4-6 comparable companies and capture market multiples.
• Create an assumptions tab with revenue, margin, capex, working capital, WACC, and terminal growth.

1. Build historical financials and clean one-off items.
2. Forecast revenue, costs, taxes, capex, and working capital.
3. Calculate DCF value, trading comps value, and transaction comps value where possible.
4. Run sensitivity on WACC, terminal growth, exit multiple, and margin assumptions.
5. Write a final recommendation explaining upside/downside, key risks, and what would change your view.

• Professional brief
• Financial model or analytical workbook
• Presentation deck
• Source log
• Interview talking points
• Source and assumption log
• One-page executive summary
• Final output file

• Public filings, official regulatory material, public datasets, company career/domain research, and a documented source log.
• Latest annual report
• Quarterly results or investor presentation
• NSE/BSE price history
• Peer company filings
• Public transaction/news sources

• Clear problem statement and role context
• Source-backed assumptions
• Clean model, memo, notebook, or dashboard
• Decision recommendation with risks
• Resume bullet and interview talk track

• Problem: explain the business question and why it matters for Venture Capital.
• Method: describe the data collected, assumptions made, and analysis performed.
• Decision: state the recommendation, key risk, and what would change your view.

Act as a junior Venture Capital analyst. Use public information to produce a decision-ready work product for a manager or interview panel.

Show that you can apply Deal sourcing, Unit economics, Fundraising and cap tables, Investment memo writing, Finance product management in a practical analyst workflow, not only explain the theory.

• Pick one listed company and define the valuation date.
• Collect financial statements for at least three years.
• Choose 4-6 comparable companies and capture market multiples.
• Create an assumptions tab with revenue, margin, capex, working capital, WACC, and terminal growth.

1. Build historical financials and clean one-off items.
2. Forecast revenue, costs, taxes, capex, and working capital.
3. Calculate DCF value, trading comps value, and transaction comps value where possible.
4. Run sensitivity on WACC, terminal growth, exit multiple, and margin assumptions.
5. Write a final recommendation explaining upside/downside, key risks, and what would change your view.

• Professional brief
• Financial model or analytical workbook
• Presentation deck
• Source log
• Interview talking points
• Source and assumption log
• One-page executive summary
• Final output file

• Public filings, official regulatory material, public datasets, company career/domain research, and a documented source log.
• Latest annual report
• Quarterly results or investor presentation
• NSE/BSE price history
• Peer company filings
• Public transaction/news sources

• Clear problem statement and role context
• Source-backed assumptions
• Clean model, memo, notebook, or dashboard
• Decision recommendation with risks
• Resume bullet and interview talk track

• Problem: explain the business question and why it matters for Venture Capital.
• Method: describe the data collected, assumptions made, and analysis performed.
• Decision: state the recommendation, key risk, and what would change your view.

Act as a junior Equity Research analyst. Use public information to produce a decision-ready work product for a manager or interview panel.

Show that you can apply Accounting fluency, Valuation, Equity research, Research writing, Financial modeling in a practical analyst workflow, not only explain the theory.

• Pick one listed company and define the valuation date.
• Collect financial statements for at least three years.
• Choose 4-6 comparable companies and capture market multiples.
• Create an assumptions tab with revenue, margin, capex, working capital, WACC, and terminal growth.

1. Build historical financials and clean one-off items.
2. Forecast revenue, costs, taxes, capex, and working capital.
3. Calculate DCF value, trading comps value, and transaction comps value where possible.
4. Run sensitivity on WACC, terminal growth, exit multiple, and margin assumptions.
5. Write a final recommendation explaining upside/downside, key risks, and what would change your view.

• Professional brief
• Financial model or analytical workbook
• Presentation deck
• Source log
• Interview talking points
• Source and assumption log
• One-page executive summary
• Final output file

• Annual reports, investor presentations, exchange filings, SEBI/RBI/NSE/BSE pages, and official company disclosures.
• Latest annual report
• Quarterly results or investor presentation
• NSE/BSE price history
• Peer company filings
• Public transaction/news sources

• Clear problem statement and role context
• Source-backed assumptions
• Clean model, memo, notebook, or dashboard
• Decision recommendation with risks
• Resume bullet and interview talk track

• Problem: explain the business question and why it matters for Equity Research.
• Method: describe the data collected, assumptions made, and analysis performed.
• Decision: state the recommendation, key risk, and what would change your view.

Act as a junior Equity Research analyst. Use public information to produce a decision-ready work product for a manager or interview panel.

Show that you can apply Accounting fluency, Valuation, Equity research, Research writing, Financial modeling in a practical analyst workflow, not only explain the theory.

• Pick one listed company and define the valuation date.
• Collect financial statements for at least three years.
• Choose 4-6 comparable companies and capture market multiples.
• Create an assumptions tab with revenue, margin, capex, working capital, WACC, and terminal growth.

1. Build historical financials and clean one-off items.
2. Forecast revenue, costs, taxes, capex, and working capital.
3. Calculate DCF value, trading comps value, and transaction comps value where possible.
4. Run sensitivity on WACC, terminal growth, exit multiple, and margin assumptions.
5. Write a final recommendation explaining upside/downside, key risks, and what would change your view.

• Professional brief
• Financial model or analytical workbook
• Presentation deck
• Source log
• Interview talking points
• Source and assumption log
• One-page executive summary
• Final output file

• Public transaction announcements, DRHP/RHP filings, exchange releases, rating reports, and company presentations.
• Latest annual report
• Quarterly results or investor presentation
• NSE/BSE price history
• Peer company filings
• Public transaction/news sources

• Clear problem statement and role context
• Source-backed assumptions
• Clean model, memo, notebook, or dashboard
• Decision recommendation with risks
• Resume bullet and interview talk track

• Problem: explain the business question and why it matters for Equity Research.
• Method: describe the data collected, assumptions made, and analysis performed.
• Decision: state the recommendation, key risk, and what would change your view.

Act as a junior Equity Research analyst. Use public information to produce a decision-ready work product for a manager or interview panel.

Show that you can apply Accounting fluency, Valuation, Equity research, Research writing, Financial modeling in a practical analyst workflow, not only explain the theory.

• Pick one listed company and define the valuation date.
• Collect financial statements for at least three years.
• Choose 4-6 comparable companies and capture market multiples.
• Create an assumptions tab with revenue, margin, capex, working capital, WACC, and terminal growth.

1. Build historical financials and clean one-off items.
2. Forecast revenue, costs, taxes, capex, and working capital.
3. Calculate DCF value, trading comps value, and transaction comps value where possible.
4. Run sensitivity on WACC, terminal growth, exit multiple, and margin assumptions.
5. Write a final recommendation explaining upside/downside, key risks, and what would change your view.

• Professional brief
• Financial model or analytical workbook
• Presentation deck
• Source log
• Interview talking points
• Source and assumption log
• One-page executive summary
• Final output file

• Public filings, official regulatory material, public datasets, company career/domain research, and a documented source log.
• Latest annual report
• Quarterly results or investor presentation
• NSE/BSE price history
• Peer company filings
• Public transaction/news sources

• Clear problem statement and role context
• Source-backed assumptions
• Clean model, memo, notebook, or dashboard
• Decision recommendation with risks
• Resume bullet and interview talk track

• Problem: explain the business question and why it matters for Equity Research.
• Method: describe the data collected, assumptions made, and analysis performed.
• Decision: state the recommendation, key risk, and what would change your view.

Act as a junior Equity Research analyst. Use public information to produce a decision-ready work product for a manager or interview panel.

Show that you can apply Accounting fluency, Valuation, Equity research, Research writing, Financial modeling in a practical analyst workflow, not only explain the theory.

• Pick one listed company and define the valuation date.
• Collect financial statements for at least three years.
• Choose 4-6 comparable companies and capture market multiples.
• Create an assumptions tab with revenue, margin, capex, working capital, WACC, and terminal growth.

1. Build historical financials and clean one-off items.
2. Forecast revenue, costs, taxes, capex, and working capital.
3. Calculate DCF value, trading comps value, and transaction comps value where possible.
4. Run sensitivity on WACC, terminal growth, exit multiple, and margin assumptions.
5. Write a final recommendation explaining upside/downside, key risks, and what would change your view.

• Professional brief
• Financial model or analytical workbook
• Presentation deck
• Source log
• Interview talking points
• Source and assumption log
• One-page executive summary
• Final output file

• Public filings, official regulatory material, public datasets, company career/domain research, and a documented source log.
• Latest annual report
• Quarterly results or investor presentation
• NSE/BSE price history
• Peer company filings
• Public transaction/news sources

• Clear problem statement and role context
• Source-backed assumptions
• Clean model, memo, notebook, or dashboard
• Decision recommendation with risks
• Resume bullet and interview talk track

• Problem: explain the business question and why it matters for Equity Research.
• Method: describe the data collected, assumptions made, and analysis performed.
• Decision: state the recommendation, key risk, and what would change your view.

Act as a junior Equity Research analyst. Use public information to produce a decision-ready work product for a manager or interview panel.

Show that you can apply Accounting fluency, Valuation, Equity research, Research writing, Financial modeling in a practical analyst workflow, not only explain the theory.

• Pick one listed company and define the valuation date.
• Collect financial statements for at least three years.
• Choose 4-6 comparable companies and capture market multiples.
• Create an assumptions tab with revenue, margin, capex, working capital, WACC, and terminal growth.

1. Build historical financials and clean one-off items.
2. Forecast revenue, costs, taxes, capex, and working capital.
3. Calculate DCF value, trading comps value, and transaction comps value where possible.
4. Run sensitivity on WACC, terminal growth, exit multiple, and margin assumptions.
5. Write a final recommendation explaining upside/downside, key risks, and what would change your view.

• Professional brief
• Financial model or analytical workbook
• Presentation deck
• Source log
• Interview talking points
• Source and assumption log
• One-page executive summary
• Final output file

• Public filings, official regulatory material, public datasets, company career/domain research, and a documented source log.
• Latest annual report
• Quarterly results or investor presentation
• NSE/BSE price history
• Peer company filings
• Public transaction/news sources

• Clear problem statement and role context
• Source-backed assumptions
• Clean model, memo, notebook, or dashboard
• Decision recommendation with risks
• Resume bullet and interview talk track

• Problem: explain the business question and why it matters for Equity Research.
• Method: describe the data collected, assumptions made, and analysis performed.
• Decision: state the recommendation, key risk, and what would change your view.

Act as a junior Asset Management analyst. Use public information to produce a decision-ready work product for a manager or interview panel.

Show that you can apply Portfolio construction, Equity research, Fixed income, Risk modeling, Valuation in a practical analyst workflow, not only explain the theory.

• Pick one listed company and define the valuation date.
• Collect financial statements for at least three years.
• Choose 4-6 comparable companies and capture market multiples.
• Create an assumptions tab with revenue, margin, capex, working capital, WACC, and terminal growth.

1. Build historical financials and clean one-off items.
2. Forecast revenue, costs, taxes, capex, and working capital.
3. Calculate DCF value, trading comps value, and transaction comps value where possible.
4. Run sensitivity on WACC, terminal growth, exit multiple, and margin assumptions.
5. Write a final recommendation explaining upside/downside, key risks, and what would change your view.

• Professional brief
• Financial model or analytical workbook
• Presentation deck
• Source log
• Interview talking points
• Source and assumption log
• One-page executive summary
• Final output file

• Annual reports, investor presentations, exchange filings, SEBI/RBI/NSE/BSE pages, and official company disclosures.
• Latest annual report
• Quarterly results or investor presentation
• NSE/BSE price history
• Peer company filings
• Public transaction/news sources

• Clear problem statement and role context
• Source-backed assumptions
• Clean model, memo, notebook, or dashboard
• Decision recommendation with risks
• Resume bullet and interview talk track

• Problem: explain the business question and why it matters for Asset Management.
• Method: describe the data collected, assumptions made, and analysis performed.
• Decision: state the recommendation, key risk, and what would change your view.

Act as a junior Asset Management analyst. Use public information to produce a decision-ready work product for a manager or interview panel.

Show that you can apply Portfolio construction, Equity research, Fixed income, Risk modeling, Valuation in a practical analyst workflow, not only explain the theory.

• Pick one listed company and define the valuation date.
• Collect financial statements for at least three years.
• Choose 4-6 comparable companies and capture market multiples.
• Create an assumptions tab with revenue, margin, capex, working capital, WACC, and terminal growth.

1. Build historical financials and clean one-off items.
2. Forecast revenue, costs, taxes, capex, and working capital.
3. Calculate DCF value, trading comps value, and transaction comps value where possible.
4. Run sensitivity on WACC, terminal growth, exit multiple, and margin assumptions.
5. Write a final recommendation explaining upside/downside, key risks, and what would change your view.

• Professional brief
• Financial model or analytical workbook
• Presentation deck
• Source log
• Interview talking points
• Source and assumption log
• One-page executive summary
• Final output file

• Public transaction announcements, DRHP/RHP filings, exchange releases, rating reports, and company presentations.
• Latest annual report
• Quarterly results or investor presentation
• NSE/BSE price history
• Peer company filings
• Public transaction/news sources

• Clear problem statement and role context
• Source-backed assumptions
• Clean model, memo, notebook, or dashboard
• Decision recommendation with risks
• Resume bullet and interview talk track

• Problem: explain the business question and why it matters for Asset Management.
• Method: describe the data collected, assumptions made, and analysis performed.
• Decision: state the recommendation, key risk, and what would change your view.

Act as a junior Asset Management analyst. Use public information to produce a decision-ready work product for a manager or interview panel.

Show that you can apply Portfolio construction, Equity research, Fixed income, Risk modeling, Valuation in a practical analyst workflow, not only explain the theory.

• Pick one listed company and define the valuation date.
• Collect financial statements for at least three years.
• Choose 4-6 comparable companies and capture market multiples.
• Create an assumptions tab with revenue, margin, capex, working capital, WACC, and terminal growth.

1. Build historical financials and clean one-off items.
2. Forecast revenue, costs, taxes, capex, and working capital.
3. Calculate DCF value, trading comps value, and transaction comps value where possible.
4. Run sensitivity on WACC, terminal growth, exit multiple, and margin assumptions.
5. Write a final recommendation explaining upside/downside, key risks, and what would change your view.

• Professional brief
• Financial model or analytical workbook
• Presentation deck
• Source log
• Interview talking points
• Source and assumption log
• One-page executive summary
• Final output file

• Public filings, official regulatory material, public datasets, company career/domain research, and a documented source log.
• Latest annual report
• Quarterly results or investor presentation
• NSE/BSE price history
• Peer company filings
• Public transaction/news sources

• Clear problem statement and role context
• Source-backed assumptions
• Clean model, memo, notebook, or dashboard
• Decision recommendation with risks
• Resume bullet and interview talk track

• Problem: explain the business question and why it matters for Asset Management.
• Method: describe the data collected, assumptions made, and analysis performed.
• Decision: state the recommendation, key risk, and what would change your view.

Act as a junior Asset Management analyst. Use public information to produce a decision-ready work product for a manager or interview panel.

Show that you can apply Portfolio construction, Equity research, Fixed income, Risk modeling, Valuation in a practical analyst workflow, not only explain the theory.

• Pick one listed company and define the valuation date.
• Collect financial statements for at least three years.
• Choose 4-6 comparable companies and capture market multiples.
• Create an assumptions tab with revenue, margin, capex, working capital, WACC, and terminal growth.

1. Build historical financials and clean one-off items.
2. Forecast revenue, costs, taxes, capex, and working capital.
3. Calculate DCF value, trading comps value, and transaction comps value where possible.
4. Run sensitivity on WACC, terminal growth, exit multiple, and margin assumptions.
5. Write a final recommendation explaining upside/downside, key risks, and what would change your view.

• Professional brief
• Financial model or analytical workbook
• Presentation deck
• Source log
• Interview talking points
• Source and assumption log
• One-page executive summary
• Final output file

• Public filings, official regulatory material, public datasets, company career/domain research, and a documented source log.
• Latest annual report
• Quarterly results or investor presentation
• NSE/BSE price history
• Peer company filings
• Public transaction/news sources

• Clear problem statement and role context
• Source-backed assumptions
• Clean model, memo, notebook, or dashboard
• Decision recommendation with risks
• Resume bullet and interview talk track

• Problem: explain the business question and why it matters for Asset Management.
• Method: describe the data collected, assumptions made, and analysis performed.
• Decision: state the recommendation, key risk, and what would change your view.

Act as a junior Asset Management analyst. Use public information to produce a decision-ready work product for a manager or interview panel.

Show that you can apply Portfolio construction, Equity research, Fixed income, Risk modeling, Valuation in a practical analyst workflow, not only explain the theory.

• Pick one listed company and define the valuation date.
• Collect financial statements for at least three years.
• Choose 4-6 comparable companies and capture market multiples.
• Create an assumptions tab with revenue, margin, capex, working capital, WACC, and terminal growth.

1. Build historical financials and clean one-off items.
2. Forecast revenue, costs, taxes, capex, and working capital.
3. Calculate DCF value, trading comps value, and transaction comps value where possible.
4. Run sensitivity on WACC, terminal growth, exit multiple, and margin assumptions.
5. Write a final recommendation explaining upside/downside, key risks, and what would change your view.

• Professional brief
• Financial model or analytical workbook
• Presentation deck
• Source log
• Interview talking points
• Source and assumption log
• One-page executive summary
• Final output file

• Public filings, official regulatory material, public datasets, company career/domain research, and a documented source log.
• Latest annual report
• Quarterly results or investor presentation
• NSE/BSE price history
• Peer company filings
• Public transaction/news sources

• Clear problem statement and role context
• Source-backed assumptions
• Clean model, memo, notebook, or dashboard
• Decision recommendation with risks
• Resume bullet and interview talk track

• Problem: explain the business question and why it matters for Asset Management.
• Method: describe the data collected, assumptions made, and analysis performed.
• Decision: state the recommendation, key risk, and what would change your view.

Act as a junior Private Wealth and Wealth Management analyst. Use public information to produce a decision-ready work product for a manager or interview panel.

Show that you can apply Client advisory, Portfolio construction, Indian financial regulation, Fixed income, Tax and estate planning in a practical analyst workflow, not only explain the theory.

• Define the hypothesis before coding.
• Select universe, date range, frequency, and benchmark.
• Create a reproducible notebook structure: import, clean, signal, test, evaluate, explain.

1. Clean and validate the dataset.
2. Build signals or factors with no look-ahead bias.
3. Run backtest with transaction-cost assumptions.
4. Calculate returns, volatility, drawdown, Sharpe, hit rate, beta, and benchmark comparison.
5. Write a limitations section explaining survivorship, data quality, and overfitting risk.

• Professional brief
• Financial model or analytical workbook
• Presentation deck
• Source log
• Interview talking points
• Source and assumption log
• One-page executive summary
• Final output file

• Annual reports, investor presentations, exchange filings, SEBI/RBI/NSE/BSE pages, and official company disclosures.
• Yahoo Finance or Stooq price data
• NSE/BSE public data
• FRED or World Bank macro data
• Company fundamentals from public filings
• Synthetic data when licensing blocks redistribution

• Clear problem statement and role context
• Source-backed assumptions
• Clean model, memo, notebook, or dashboard
• Decision recommendation with risks
• Resume bullet and interview talk track

• Problem: explain the business question and why it matters for Private Wealth and Wealth Management.
• Method: describe the data collected, assumptions made, and analysis performed.
• Decision: state the recommendation, key risk, and what would change your view.

Act as a junior Private Wealth and Wealth Management analyst. Use public information to produce a decision-ready work product for a manager or interview panel.

Show that you can apply Client advisory, Portfolio construction, Indian financial regulation, Fixed income, Tax and estate planning in a practical analyst workflow, not only explain the theory.

• Define the hypothesis before coding.
• Select universe, date range, frequency, and benchmark.
• Create a reproducible notebook structure: import, clean, signal, test, evaluate, explain.

1. Clean and validate the dataset.
2. Build signals or factors with no look-ahead bias.
3. Run backtest with transaction-cost assumptions.
4. Calculate returns, volatility, drawdown, Sharpe, hit rate, beta, and benchmark comparison.
5. Write a limitations section explaining survivorship, data quality, and overfitting risk.

• Professional brief
• Financial model or analytical workbook
• Presentation deck
• Source log
• Interview talking points
• Source and assumption log
• One-page executive summary
• Final output file

• Public transaction announcements, DRHP/RHP filings, exchange releases, rating reports, and company presentations.
• Yahoo Finance or Stooq price data
• NSE/BSE public data
• FRED or World Bank macro data
• Company fundamentals from public filings
• Synthetic data when licensing blocks redistribution

• Clear problem statement and role context
• Source-backed assumptions
• Clean model, memo, notebook, or dashboard
• Decision recommendation with risks
• Resume bullet and interview talk track

• Problem: explain the business question and why it matters for Private Wealth and Wealth Management.
• Method: describe the data collected, assumptions made, and analysis performed.
• Decision: state the recommendation, key risk, and what would change your view.

Act as a junior Private Wealth and Wealth Management analyst. Use public information to produce a decision-ready work product for a manager or interview panel.

Show that you can apply Client advisory, Portfolio construction, Indian financial regulation, Fixed income, Tax and estate planning in a practical analyst workflow, not only explain the theory.

• Define the hypothesis before coding.
• Select universe, date range, frequency, and benchmark.
• Create a reproducible notebook structure: import, clean, signal, test, evaluate, explain.

1. Clean and validate the dataset.
2. Build signals or factors with no look-ahead bias.
3. Run backtest with transaction-cost assumptions.
4. Calculate returns, volatility, drawdown, Sharpe, hit rate, beta, and benchmark comparison.
5. Write a limitations section explaining survivorship, data quality, and overfitting risk.

• Professional brief
• Financial model or analytical workbook
• Presentation deck
• Source log
• Interview talking points
• Source and assumption log
• One-page executive summary
• Final output file

• Public filings, official regulatory material, public datasets, company career/domain research, and a documented source log.
• Yahoo Finance or Stooq price data
• NSE/BSE public data
• FRED or World Bank macro data
• Company fundamentals from public filings
• Synthetic data when licensing blocks redistribution

• Clear problem statement and role context
• Source-backed assumptions
• Clean model, memo, notebook, or dashboard
• Decision recommendation with risks
• Resume bullet and interview talk track

• Problem: explain the business question and why it matters for Private Wealth and Wealth Management.
• Method: describe the data collected, assumptions made, and analysis performed.
• Decision: state the recommendation, key risk, and what would change your view.

Act as a junior Private Wealth and Wealth Management analyst. Use public information to produce a decision-ready work product for a manager or interview panel.

Show that you can apply Client advisory, Portfolio construction, Indian financial regulation, Fixed income, Tax and estate planning in a practical analyst workflow, not only explain the theory.

• Define the hypothesis before coding.
• Select universe, date range, frequency, and benchmark.
• Create a reproducible notebook structure: import, clean, signal, test, evaluate, explain.

1. Clean and validate the dataset.
2. Build signals or factors with no look-ahead bias.
3. Run backtest with transaction-cost assumptions.
4. Calculate returns, volatility, drawdown, Sharpe, hit rate, beta, and benchmark comparison.
5. Write a limitations section explaining survivorship, data quality, and overfitting risk.

• Professional brief
• Financial model or analytical workbook
• Presentation deck
• Source log
• Interview talking points
• Source and assumption log
• One-page executive summary
• Final output file

• Public filings, official regulatory material, public datasets, company career/domain research, and a documented source log.
• Yahoo Finance or Stooq price data
• NSE/BSE public data
• FRED or World Bank macro data
• Company fundamentals from public filings
• Synthetic data when licensing blocks redistribution

• Clear problem statement and role context
• Source-backed assumptions
• Clean model, memo, notebook, or dashboard
• Decision recommendation with risks
• Resume bullet and interview talk track

• Problem: explain the business question and why it matters for Private Wealth and Wealth Management.
• Method: describe the data collected, assumptions made, and analysis performed.
• Decision: state the recommendation, key risk, and what would change your view.

Act as a junior Private Wealth and Wealth Management analyst. Use public information to produce a decision-ready work product for a manager or interview panel.

Show that you can apply Client advisory, Portfolio construction, Indian financial regulation, Fixed income, Tax and estate planning in a practical analyst workflow, not only explain the theory.

• Define the hypothesis before coding.
• Select universe, date range, frequency, and benchmark.
• Create a reproducible notebook structure: import, clean, signal, test, evaluate, explain.

1. Clean and validate the dataset.
2. Build signals or factors with no look-ahead bias.
3. Run backtest with transaction-cost assumptions.
4. Calculate returns, volatility, drawdown, Sharpe, hit rate, beta, and benchmark comparison.
5. Write a limitations section explaining survivorship, data quality, and overfitting risk.

• Professional brief
• Financial model or analytical workbook
• Presentation deck
• Source log
• Interview talking points
• Source and assumption log
• One-page executive summary
• Final output file

• Public filings, official regulatory material, public datasets, company career/domain research, and a documented source log.
• Yahoo Finance or Stooq price data
• NSE/BSE public data
• FRED or World Bank macro data
• Company fundamentals from public filings
• Synthetic data when licensing blocks redistribution

• Clear problem statement and role context
• Source-backed assumptions
• Clean model, memo, notebook, or dashboard
• Decision recommendation with risks
• Resume bullet and interview talk track

• Problem: explain the business question and why it matters for Private Wealth and Wealth Management.
• Method: describe the data collected, assumptions made, and analysis performed.
• Decision: state the recommendation, key risk, and what would change your view.

Act as a junior Corporate Finance analyst. Use public information to produce a decision-ready work product for a manager or interview panel.

Show that you can apply FP&A, Treasury management, Financial modeling, Power BI, Investor relations in a practical analyst workflow, not only explain the theory.

• Define the business model and key operating drivers.
• Create clean input, historicals, forecast, output, and checks tabs.
• Separate assumptions from formulas.
• Decide base, upside, and downside scenarios before modeling.

1. Build historical P&L, balance sheet, and cash flow lines.
2. Create drivers for revenue, cost, margin, capex, working capital, and debt.
3. Link the statements and add balance checks.
4. Create scenario and sensitivity outputs.
5. Write a finance-manager style summary with the main movement, risk, and decision required.

• Professional brief
• Financial model or analytical workbook
• Presentation deck
• Source log
• Interview talking points
• Source and assumption log
• One-page executive summary
• Final output file

• Annual reports, investor presentations, exchange filings, SEBI/RBI/NSE/BSE pages, and official company disclosures.
• Company annual report
• Quarterly filings
• Investor presentation
• Public industry data
• Synthetic operating dataset if company data is unavailable

• Clear problem statement and role context
• Source-backed assumptions
• Clean model, memo, notebook, or dashboard
• Decision recommendation with risks
• Resume bullet and interview talk track

• Problem: explain the business question and why it matters for Corporate Finance.
• Method: describe the data collected, assumptions made, and analysis performed.
• Decision: state the recommendation, key risk, and what would change your view.

Act as a junior Corporate Finance analyst. Use public information to produce a decision-ready work product for a manager or interview panel.

Show that you can apply FP&A, Treasury management, Financial modeling, Power BI, Investor relations in a practical analyst workflow, not only explain the theory.

• Define the business model and key operating drivers.
• Create clean input, historicals, forecast, output, and checks tabs.
• Separate assumptions from formulas.
• Decide base, upside, and downside scenarios before modeling.

1. Build historical P&L, balance sheet, and cash flow lines.
2. Create drivers for revenue, cost, margin, capex, working capital, and debt.
3. Link the statements and add balance checks.
4. Create scenario and sensitivity outputs.
5. Write a finance-manager style summary with the main movement, risk, and decision required.

• Professional brief
• Financial model or analytical workbook
• Presentation deck
• Source log
• Interview talking points
• Source and assumption log
• One-page executive summary
• Final output file

• Public transaction announcements, DRHP/RHP filings, exchange releases, rating reports, and company presentations.
• Company annual report
• Quarterly filings
• Investor presentation
• Public industry data
• Synthetic operating dataset if company data is unavailable

• Clear problem statement and role context
• Source-backed assumptions
• Clean model, memo, notebook, or dashboard
• Decision recommendation with risks
• Resume bullet and interview talk track

• Problem: explain the business question and why it matters for Corporate Finance.
• Method: describe the data collected, assumptions made, and analysis performed.
• Decision: state the recommendation, key risk, and what would change your view.

Act as a junior Corporate Finance analyst. Use public information to produce a decision-ready work product for a manager or interview panel.

Show that you can apply FP&A, Treasury management, Financial modeling, Power BI, Investor relations in a practical analyst workflow, not only explain the theory.

• Define the business model and key operating drivers.
• Create clean input, historicals, forecast, output, and checks tabs.
• Separate assumptions from formulas.
• Decide base, upside, and downside scenarios before modeling.

1. Build historical P&L, balance sheet, and cash flow lines.
2. Create drivers for revenue, cost, margin, capex, working capital, and debt.
3. Link the statements and add balance checks.
4. Create scenario and sensitivity outputs.
5. Write a finance-manager style summary with the main movement, risk, and decision required.

• Professional brief
• Financial model or analytical workbook
• Presentation deck
• Source log
• Interview talking points
• Source and assumption log
• One-page executive summary
• Final output file

• Public filings, official regulatory material, public datasets, company career/domain research, and a documented source log.
• Company annual report
• Quarterly filings
• Investor presentation
• Public industry data
• Synthetic operating dataset if company data is unavailable

• Clear problem statement and role context
• Source-backed assumptions
• Clean model, memo, notebook, or dashboard
• Decision recommendation with risks
• Resume bullet and interview talk track

• Problem: explain the business question and why it matters for Corporate Finance.
• Method: describe the data collected, assumptions made, and analysis performed.
• Decision: state the recommendation, key risk, and what would change your view.

Act as a junior Corporate Finance analyst. Use public information to produce a decision-ready work product for a manager or interview panel.

Show that you can apply FP&A, Treasury management, Financial modeling, Power BI, Investor relations in a practical analyst workflow, not only explain the theory.

• Define the business model and key operating drivers.
• Create clean input, historicals, forecast, output, and checks tabs.
• Separate assumptions from formulas.
• Decide base, upside, and downside scenarios before modeling.

1. Build historical P&L, balance sheet, and cash flow lines.
2. Create drivers for revenue, cost, margin, capex, working capital, and debt.
3. Link the statements and add balance checks.
4. Create scenario and sensitivity outputs.
5. Write a finance-manager style summary with the main movement, risk, and decision required.

• Professional brief
• Financial model or analytical workbook
• Presentation deck
• Source log
• Interview talking points
• Source and assumption log
• One-page executive summary
• Final output file

• Public filings, official regulatory material, public datasets, company career/domain research, and a documented source log.
• Company annual report
• Quarterly filings
• Investor presentation
• Public industry data
• Synthetic operating dataset if company data is unavailable

• Clear problem statement and role context
• Source-backed assumptions
• Clean model, memo, notebook, or dashboard
• Decision recommendation with risks
• Resume bullet and interview talk track

• Problem: explain the business question and why it matters for Corporate Finance.
• Method: describe the data collected, assumptions made, and analysis performed.
• Decision: state the recommendation, key risk, and what would change your view.

Act as a junior Corporate Finance analyst. Use public information to produce a decision-ready work product for a manager or interview panel.

Show that you can apply FP&A, Treasury management, Financial modeling, Power BI, Investor relations in a practical analyst workflow, not only explain the theory.

• Define the business model and key operating drivers.
• Create clean input, historicals, forecast, output, and checks tabs.
• Separate assumptions from formulas.
• Decide base, upside, and downside scenarios before modeling.

1. Build historical P&L, balance sheet, and cash flow lines.
2. Create drivers for revenue, cost, margin, capex, working capital, and debt.
3. Link the statements and add balance checks.
4. Create scenario and sensitivity outputs.
5. Write a finance-manager style summary with the main movement, risk, and decision required.

• Professional brief
• Financial model or analytical workbook
• Presentation deck
• Source log
• Interview talking points
• Source and assumption log
• One-page executive summary
• Final output file

• Public filings, official regulatory material, public datasets, company career/domain research, and a documented source log.
• Company annual report
• Quarterly filings
• Investor presentation
• Public industry data
• Synthetic operating dataset if company data is unavailable

• Clear problem statement and role context
• Source-backed assumptions
• Clean model, memo, notebook, or dashboard
• Decision recommendation with risks
• Resume bullet and interview talk track

• Problem: explain the business question and why it matters for Corporate Finance.
• Method: describe the data collected, assumptions made, and analysis performed.
• Decision: state the recommendation, key risk, and what would change your view.

Act as a junior Capital Markets and Sales Trading analyst. Use public information to produce a decision-ready work product for a manager or interview panel.

Show that you can apply Market microstructure, Derivatives, Technical analysis, Fixed income, Risk modeling in a practical analyst workflow, not only explain the theory.

• Define the portfolio, exposure, product, or process under review.
• Choose risk metrics such as VaR, drawdown, volatility, PD/LGD, limits, or loss frequency.
• Set data assumptions and limitations upfront.

1. Clean the dataset and define risk factors.
2. Calculate baseline risk metrics and limit usage.
3. Run stress scenarios and compare against risk appetite.
4. Create a dashboard or memo explaining breaches and trends.
5. Recommend actions such as hedge, reduce, monitor, escalate, or change controls.

• Professional brief
• Financial model or analytical workbook
• Presentation deck
• Source log
• Interview talking points
• Source and assumption log
• One-page executive summary
• Final memo PDF

• Annual reports, investor presentations, exchange filings, SEBI/RBI/NSE/BSE pages, and official company disclosures.
• NSE/BSE market data
• RBI/SEBI circulars where relevant
• Public historical price data
• Synthetic loss-event dataset
• Company risk disclosures

• Clear problem statement and role context
• Source-backed assumptions
• Clean model, memo, notebook, or dashboard
• Decision recommendation with risks
• Resume bullet and interview talk track

• Problem: explain the business question and why it matters for Capital Markets and Sales Trading.
• Method: describe the data collected, assumptions made, and analysis performed.
• Decision: state the recommendation, key risk, and what would change your view.

Act as a junior Capital Markets and Sales Trading analyst. Use public information to produce a decision-ready work product for a manager or interview panel.

Show that you can apply Market microstructure, Derivatives, Technical analysis, Fixed income, Risk modeling in a practical analyst workflow, not only explain the theory.

• Define the portfolio, exposure, product, or process under review.
• Choose risk metrics such as VaR, drawdown, volatility, PD/LGD, limits, or loss frequency.
• Set data assumptions and limitations upfront.

1. Clean the dataset and define risk factors.
2. Calculate baseline risk metrics and limit usage.
3. Run stress scenarios and compare against risk appetite.
4. Create a dashboard or memo explaining breaches and trends.
5. Recommend actions such as hedge, reduce, monitor, escalate, or change controls.

• Professional brief
• Financial model or analytical workbook
• Presentation deck
• Source log
• Interview talking points
• Source and assumption log
• One-page executive summary
• Final memo PDF

• Public transaction announcements, DRHP/RHP filings, exchange releases, rating reports, and company presentations.
• NSE/BSE market data
• RBI/SEBI circulars where relevant
• Public historical price data
• Synthetic loss-event dataset
• Company risk disclosures

• Clear problem statement and role context
• Source-backed assumptions
• Clean model, memo, notebook, or dashboard
• Decision recommendation with risks
• Resume bullet and interview talk track

• Problem: explain the business question and why it matters for Capital Markets and Sales Trading.
• Method: describe the data collected, assumptions made, and analysis performed.
• Decision: state the recommendation, key risk, and what would change your view.

Act as a junior Capital Markets and Sales Trading analyst. Use public information to produce a decision-ready work product for a manager or interview panel.

Show that you can apply Market microstructure, Derivatives, Technical analysis, Fixed income, Risk modeling in a practical analyst workflow, not only explain the theory.

• Define the portfolio, exposure, product, or process under review.
• Choose risk metrics such as VaR, drawdown, volatility, PD/LGD, limits, or loss frequency.
• Set data assumptions and limitations upfront.

1. Clean the dataset and define risk factors.
2. Calculate baseline risk metrics and limit usage.
3. Run stress scenarios and compare against risk appetite.
4. Create a dashboard or memo explaining breaches and trends.
5. Recommend actions such as hedge, reduce, monitor, escalate, or change controls.

• Professional brief
• Financial model or analytical workbook
• Presentation deck
• Source log
• Interview talking points
• Source and assumption log
• One-page executive summary
• Final memo PDF

• Public filings, official regulatory material, public datasets, company career/domain research, and a documented source log.
• NSE/BSE market data
• RBI/SEBI circulars where relevant
• Public historical price data
• Synthetic loss-event dataset
• Company risk disclosures

• Clear problem statement and role context
• Source-backed assumptions
• Clean model, memo, notebook, or dashboard
• Decision recommendation with risks
• Resume bullet and interview talk track

• Problem: explain the business question and why it matters for Capital Markets and Sales Trading.
• Method: describe the data collected, assumptions made, and analysis performed.
• Decision: state the recommendation, key risk, and what would change your view.

Act as a junior Capital Markets and Sales Trading analyst. Use public information to produce a decision-ready work product for a manager or interview panel.

Show that you can apply Market microstructure, Derivatives, Technical analysis, Fixed income, Risk modeling in a practical analyst workflow, not only explain the theory.

• Define the portfolio, exposure, product, or process under review.
• Choose risk metrics such as VaR, drawdown, volatility, PD/LGD, limits, or loss frequency.
• Set data assumptions and limitations upfront.

1. Clean the dataset and define risk factors.
2. Calculate baseline risk metrics and limit usage.
3. Run stress scenarios and compare against risk appetite.
4. Create a dashboard or memo explaining breaches and trends.
5. Recommend actions such as hedge, reduce, monitor, escalate, or change controls.

• Professional brief
• Financial model or analytical workbook
• Presentation deck
• Source log
• Interview talking points
• Source and assumption log
• One-page executive summary
• Final memo PDF

• Public filings, official regulatory material, public datasets, company career/domain research, and a documented source log.
• NSE/BSE market data
• RBI/SEBI circulars where relevant
• Public historical price data
• Synthetic loss-event dataset
• Company risk disclosures

• Clear problem statement and role context
• Source-backed assumptions
• Clean model, memo, notebook, or dashboard
• Decision recommendation with risks
• Resume bullet and interview talk track

• Problem: explain the business question and why it matters for Capital Markets and Sales Trading.
• Method: describe the data collected, assumptions made, and analysis performed.
• Decision: state the recommendation, key risk, and what would change your view.

Act as a junior Capital Markets and Sales Trading analyst. Use public information to produce a decision-ready work product for a manager or interview panel.

Show that you can apply Market microstructure, Derivatives, Technical analysis, Fixed income, Risk modeling in a practical analyst workflow, not only explain the theory.

• Define the portfolio, exposure, product, or process under review.
• Choose risk metrics such as VaR, drawdown, volatility, PD/LGD, limits, or loss frequency.
• Set data assumptions and limitations upfront.

1. Clean the dataset and define risk factors.
2. Calculate baseline risk metrics and limit usage.
3. Run stress scenarios and compare against risk appetite.
4. Create a dashboard or memo explaining breaches and trends.
5. Recommend actions such as hedge, reduce, monitor, escalate, or change controls.

• Professional brief
• Financial model or analytical workbook
• Presentation deck
• Source log
• Interview talking points
• Source and assumption log
• One-page executive summary
• Final memo PDF

• Public filings, official regulatory material, public datasets, company career/domain research, and a documented source log.
• NSE/BSE market data
• RBI/SEBI circulars where relevant
• Public historical price data
• Synthetic loss-event dataset
• Company risk disclosures

• Clear problem statement and role context
• Source-backed assumptions
• Clean model, memo, notebook, or dashboard
• Decision recommendation with risks
• Resume bullet and interview talk track

• Problem: explain the business question and why it matters for Capital Markets and Sales Trading.
• Method: describe the data collected, assumptions made, and analysis performed.
• Decision: state the recommendation, key risk, and what would change your view.

Act as a junior Quantitative Finance analyst. Use public information to produce a decision-ready work product for a manager or interview panel.

Show that you can apply Python, Statistics, Machine learning, Risk modeling, Derivatives in a practical analyst workflow, not only explain the theory.

• Define the portfolio, exposure, product, or process under review.
• Choose risk metrics such as VaR, drawdown, volatility, PD/LGD, limits, or loss frequency.
• Set data assumptions and limitations upfront.

1. Clean the dataset and define risk factors.
2. Calculate baseline risk metrics and limit usage.
3. Run stress scenarios and compare against risk appetite.
4. Create a dashboard or memo explaining breaches and trends.
5. Recommend actions such as hedge, reduce, monitor, escalate, or change controls.

• Professional brief
• Financial model or analytical workbook
• Presentation deck
• Source log
• Interview talking points
• Source and assumption log
• One-page executive summary
• Final memo PDF

• Annual reports, investor presentations, exchange filings, SEBI/RBI/NSE/BSE pages, and official company disclosures.
• NSE/BSE market data
• RBI/SEBI circulars where relevant
• Public historical price data
• Synthetic loss-event dataset
• Company risk disclosures

• Clear problem statement and role context
• Source-backed assumptions
• Clean model, memo, notebook, or dashboard
• Decision recommendation with risks
• Resume bullet and interview talk track

• Problem: explain the business question and why it matters for Quantitative Finance.
• Method: describe the data collected, assumptions made, and analysis performed.
• Decision: state the recommendation, key risk, and what would change your view.

Act as a junior Quantitative Finance analyst. Use public information to produce a decision-ready work product for a manager or interview panel.

Show that you can apply Python, Statistics, Machine learning, Risk modeling, Derivatives in a practical analyst workflow, not only explain the theory.

• Define the portfolio, exposure, product, or process under review.
• Choose risk metrics such as VaR, drawdown, volatility, PD/LGD, limits, or loss frequency.
• Set data assumptions and limitations upfront.

1. Clean the dataset and define risk factors.
2. Calculate baseline risk metrics and limit usage.
3. Run stress scenarios and compare against risk appetite.
4. Create a dashboard or memo explaining breaches and trends.
5. Recommend actions such as hedge, reduce, monitor, escalate, or change controls.

• Professional brief
• Financial model or analytical workbook
• Presentation deck
• Source log
• Interview talking points
• Source and assumption log
• One-page executive summary
• Final memo PDF

• Public transaction announcements, DRHP/RHP filings, exchange releases, rating reports, and company presentations.
• NSE/BSE market data
• RBI/SEBI circulars where relevant
• Public historical price data
• Synthetic loss-event dataset
• Company risk disclosures

• Clear problem statement and role context
• Source-backed assumptions
• Clean model, memo, notebook, or dashboard
• Decision recommendation with risks
• Resume bullet and interview talk track

• Problem: explain the business question and why it matters for Quantitative Finance.
• Method: describe the data collected, assumptions made, and analysis performed.
• Decision: state the recommendation, key risk, and what would change your view.

Act as a junior Quantitative Finance analyst. Use public information to produce a decision-ready work product for a manager or interview panel.

Show that you can apply Python, Statistics, Machine learning, Risk modeling, Derivatives in a practical analyst workflow, not only explain the theory.

• Define the portfolio, exposure, product, or process under review.
• Choose risk metrics such as VaR, drawdown, volatility, PD/LGD, limits, or loss frequency.
• Set data assumptions and limitations upfront.

1. Clean the dataset and define risk factors.
2. Calculate baseline risk metrics and limit usage.
3. Run stress scenarios and compare against risk appetite.
4. Create a dashboard or memo explaining breaches and trends.
5. Recommend actions such as hedge, reduce, monitor, escalate, or change controls.

• Professional brief
• Financial model or analytical workbook
• Presentation deck
• Source log
• Interview talking points
• Source and assumption log
• One-page executive summary
• Final memo PDF

• Public filings, official regulatory material, public datasets, company career/domain research, and a documented source log.
• NSE/BSE market data
• RBI/SEBI circulars where relevant
• Public historical price data
• Synthetic loss-event dataset
• Company risk disclosures

• Clear problem statement and role context
• Source-backed assumptions
• Clean model, memo, notebook, or dashboard
• Decision recommendation with risks
• Resume bullet and interview talk track

• Problem: explain the business question and why it matters for Quantitative Finance.
• Method: describe the data collected, assumptions made, and analysis performed.
• Decision: state the recommendation, key risk, and what would change your view.

Act as a junior Quantitative Finance analyst. Use public information to produce a decision-ready work product for a manager or interview panel.

Show that you can apply Python, Statistics, Machine learning, Risk modeling, Derivatives in a practical analyst workflow, not only explain the theory.

• Define the portfolio, exposure, product, or process under review.
• Choose risk metrics such as VaR, drawdown, volatility, PD/LGD, limits, or loss frequency.
• Set data assumptions and limitations upfront.

1. Clean the dataset and define risk factors.
2. Calculate baseline risk metrics and limit usage.
3. Run stress scenarios and compare against risk appetite.
4. Create a dashboard or memo explaining breaches and trends.
5. Recommend actions such as hedge, reduce, monitor, escalate, or change controls.

• Professional brief
• Financial model or analytical workbook
• Presentation deck
• Source log
• Interview talking points
• Source and assumption log
• One-page executive summary
• Final memo PDF

• Public filings, official regulatory material, public datasets, company career/domain research, and a documented source log.
• NSE/BSE market data
• RBI/SEBI circulars where relevant
• Public historical price data
• Synthetic loss-event dataset
• Company risk disclosures

• Clear problem statement and role context
• Source-backed assumptions
• Clean model, memo, notebook, or dashboard
• Decision recommendation with risks
• Resume bullet and interview talk track

• Problem: explain the business question and why it matters for Quantitative Finance.
• Method: describe the data collected, assumptions made, and analysis performed.
• Decision: state the recommendation, key risk, and what would change your view.

Act as a junior Quantitative Finance analyst. Use public information to produce a decision-ready work product for a manager or interview panel.

Show that you can apply Python, Statistics, Machine learning, Risk modeling, Derivatives in a practical analyst workflow, not only explain the theory.

• Define the portfolio, exposure, product, or process under review.
• Choose risk metrics such as VaR, drawdown, volatility, PD/LGD, limits, or loss frequency.
• Set data assumptions and limitations upfront.

1. Clean the dataset and define risk factors.
2. Calculate baseline risk metrics and limit usage.
3. Run stress scenarios and compare against risk appetite.
4. Create a dashboard or memo explaining breaches and trends.
5. Recommend actions such as hedge, reduce, monitor, escalate, or change controls.

• Professional brief
• Financial model or analytical workbook
• Presentation deck
• Source log
• Interview talking points
• Source and assumption log
• One-page executive summary
• Final memo PDF

• Public filings, official regulatory material, public datasets, company career/domain research, and a documented source log.
• NSE/BSE market data
• RBI/SEBI circulars where relevant
• Public historical price data
• Synthetic loss-event dataset
• Company risk disclosures

• Clear problem statement and role context
• Source-backed assumptions
• Clean model, memo, notebook, or dashboard
• Decision recommendation with risks
• Resume bullet and interview talk track

• Problem: explain the business question and why it matters for Quantitative Finance.
• Method: describe the data collected, assumptions made, and analysis performed.
• Decision: state the recommendation, key risk, and what would change your view.

Act as a junior Research and KPO analyst. Use public information to produce a decision-ready work product for a manager or interview panel.

Show that you can apply Financial modeling, Equity research, Credit analysis, Research writing, ESG analysis in a practical analyst workflow, not only explain the theory.

• Define the business model and key operating drivers.
• Create clean input, historicals, forecast, output, and checks tabs.
• Separate assumptions from formulas.
• Decide base, upside, and downside scenarios before modeling.

1. Build historical P&L, balance sheet, and cash flow lines.
2. Create drivers for revenue, cost, margin, capex, working capital, and debt.
3. Link the statements and add balance checks.
4. Create scenario and sensitivity outputs.
5. Write a finance-manager style summary with the main movement, risk, and decision required.

• Professional brief
• Financial model or analytical workbook
• Presentation deck
• Source log
• Interview talking points
• Source and assumption log
• One-page executive summary
• Final output file

• Annual reports, investor presentations, exchange filings, SEBI/RBI/NSE/BSE pages, and official company disclosures.
• Company annual report
• Quarterly filings
• Investor presentation
• Public industry data
• Synthetic operating dataset if company data is unavailable

• Clear problem statement and role context
• Source-backed assumptions
• Clean model, memo, notebook, or dashboard
• Decision recommendation with risks
• Resume bullet and interview talk track

• Problem: explain the business question and why it matters for Research and KPO.
• Method: describe the data collected, assumptions made, and analysis performed.
• Decision: state the recommendation, key risk, and what would change your view.

Act as a junior Research and KPO analyst. Use public information to produce a decision-ready work product for a manager or interview panel.

Show that you can apply Financial modeling, Equity research, Credit analysis, Research writing, ESG analysis in a practical analyst workflow, not only explain the theory.

• Define the business model and key operating drivers.
• Create clean input, historicals, forecast, output, and checks tabs.
• Separate assumptions from formulas.
• Decide base, upside, and downside scenarios before modeling.

1. Build historical P&L, balance sheet, and cash flow lines.
2. Create drivers for revenue, cost, margin, capex, working capital, and debt.
3. Link the statements and add balance checks.
4. Create scenario and sensitivity outputs.
5. Write a finance-manager style summary with the main movement, risk, and decision required.

• Professional brief
• Financial model or analytical workbook
• Presentation deck
• Source log
• Interview talking points
• Source and assumption log
• One-page executive summary
• Final output file

• Public transaction announcements, DRHP/RHP filings, exchange releases, rating reports, and company presentations.
• Company annual report
• Quarterly filings
• Investor presentation
• Public industry data
• Synthetic operating dataset if company data is unavailable

• Clear problem statement and role context
• Source-backed assumptions
• Clean model, memo, notebook, or dashboard
• Decision recommendation with risks
• Resume bullet and interview talk track

• Problem: explain the business question and why it matters for Research and KPO.
• Method: describe the data collected, assumptions made, and analysis performed.
• Decision: state the recommendation, key risk, and what would change your view.

Act as a junior Research and KPO analyst. Use public information to produce a decision-ready work product for a manager or interview panel.

Show that you can apply Financial modeling, Equity research, Credit analysis, Research writing, ESG analysis in a practical analyst workflow, not only explain the theory.

• Define the business model and key operating drivers.
• Create clean input, historicals, forecast, output, and checks tabs.
• Separate assumptions from formulas.
• Decide base, upside, and downside scenarios before modeling.

1. Build historical P&L, balance sheet, and cash flow lines.
2. Create drivers for revenue, cost, margin, capex, working capital, and debt.
3. Link the statements and add balance checks.
4. Create scenario and sensitivity outputs.
5. Write a finance-manager style summary with the main movement, risk, and decision required.

• Professional brief
• Financial model or analytical workbook
• Presentation deck
• Source log
• Interview talking points
• Source and assumption log
• One-page executive summary
• Final output file

• Public filings, official regulatory material, public datasets, company career/domain research, and a documented source log.
• Company annual report
• Quarterly filings
• Investor presentation
• Public industry data
• Synthetic operating dataset if company data is unavailable

• Clear problem statement and role context
• Source-backed assumptions
• Clean model, memo, notebook, or dashboard
• Decision recommendation with risks
• Resume bullet and interview talk track

• Problem: explain the business question and why it matters for Research and KPO.
• Method: describe the data collected, assumptions made, and analysis performed.
• Decision: state the recommendation, key risk, and what would change your view.

Act as a junior Research and KPO analyst. Use public information to produce a decision-ready work product for a manager or interview panel.

Show that you can apply Financial modeling, Equity research, Credit analysis, Research writing, ESG analysis in a practical analyst workflow, not only explain the theory.

• Define the business model and key operating drivers.
• Create clean input, historicals, forecast, output, and checks tabs.
• Separate assumptions from formulas.
• Decide base, upside, and downside scenarios before modeling.

1. Build historical P&L, balance sheet, and cash flow lines.
2. Create drivers for revenue, cost, margin, capex, working capital, and debt.
3. Link the statements and add balance checks.
4. Create scenario and sensitivity outputs.
5. Write a finance-manager style summary with the main movement, risk, and decision required.

• Professional brief
• Financial model or analytical workbook
• Presentation deck
• Source log
• Interview talking points
• Source and assumption log
• One-page executive summary
• Final output file

• Public filings, official regulatory material, public datasets, company career/domain research, and a documented source log.
• Company annual report
• Quarterly filings
• Investor presentation
• Public industry data
• Synthetic operating dataset if company data is unavailable

• Clear problem statement and role context
• Source-backed assumptions
• Clean model, memo, notebook, or dashboard
• Decision recommendation with risks
• Resume bullet and interview talk track

• Problem: explain the business question and why it matters for Research and KPO.
• Method: describe the data collected, assumptions made, and analysis performed.
• Decision: state the recommendation, key risk, and what would change your view.

Act as a junior Research and KPO analyst. Use public information to produce a decision-ready work product for a manager or interview panel.

Show that you can apply Financial modeling, Equity research, Credit analysis, Research writing, ESG analysis in a practical analyst workflow, not only explain the theory.

• Define the business model and key operating drivers.
• Create clean input, historicals, forecast, output, and checks tabs.
• Separate assumptions from formulas.
• Decide base, upside, and downside scenarios before modeling.

1. Build historical P&L, balance sheet, and cash flow lines.
2. Create drivers for revenue, cost, margin, capex, working capital, and debt.
3. Link the statements and add balance checks.
4. Create scenario and sensitivity outputs.
5. Write a finance-manager style summary with the main movement, risk, and decision required.

• Professional brief
• Financial model or analytical workbook
• Presentation deck
• Source log
• Interview talking points
• Source and assumption log
• One-page executive summary
• Final output file

• Public filings, official regulatory material, public datasets, company career/domain research, and a documented source log.
• Company annual report
• Quarterly filings
• Investor presentation
• Public industry data
• Synthetic operating dataset if company data is unavailable

• Clear problem statement and role context
• Source-backed assumptions
• Clean model, memo, notebook, or dashboard
• Decision recommendation with risks
• Resume bullet and interview talk track

• Problem: explain the business question and why it matters for Research and KPO.
• Method: describe the data collected, assumptions made, and analysis performed.
• Decision: state the recommendation, key risk, and what would change your view.

Act as a junior FinTech and Financial Platforms analyst. Use public information to produce a decision-ready work product for a manager or interview panel.

Show that you can apply Finance product management, Unit economics, Machine learning, SQL, Python in a practical analyst workflow, not only explain the theory.

• Define the hypothesis before coding.
• Select universe, date range, frequency, and benchmark.
• Create a reproducible notebook structure: import, clean, signal, test, evaluate, explain.

1. Clean and validate the dataset.
2. Build signals or factors with no look-ahead bias.
3. Run backtest with transaction-cost assumptions.
4. Calculate returns, volatility, drawdown, Sharpe, hit rate, beta, and benchmark comparison.
5. Write a limitations section explaining survivorship, data quality, and overfitting risk.

• Professional brief
• Financial model or analytical workbook
• Presentation deck
• Source log
• Interview talking points
• Source and assumption log
• One-page executive summary
• Final output file

• Annual reports, investor presentations, exchange filings, SEBI/RBI/NSE/BSE pages, and official company disclosures.
• Yahoo Finance or Stooq price data
• NSE/BSE public data
• FRED or World Bank macro data
• Company fundamentals from public filings
• Synthetic data when licensing blocks redistribution

• Clear problem statement and role context
• Source-backed assumptions
• Clean model, memo, notebook, or dashboard
• Decision recommendation with risks
• Resume bullet and interview talk track

• Problem: explain the business question and why it matters for FinTech and Financial Platforms.
• Method: describe the data collected, assumptions made, and analysis performed.
• Decision: state the recommendation, key risk, and what would change your view.

Act as a junior FinTech and Financial Platforms analyst. Use public information to produce a decision-ready work product for a manager or interview panel.

Show that you can apply Finance product management, Unit economics, Machine learning, SQL, Python in a practical analyst workflow, not only explain the theory.

• Pick a borrower, issuer, or sector case.
• Collect financials, debt schedule, ratings, business profile, and management commentary.
• Define the lending/rating question and time horizon.

1. Analyze revenue quality, margins, leverage, coverage, liquidity, and working capital.
2. Identify business, financial, management, and industry risks.
3. Run base and stress cases for cash flow and debt servicing.
4. Assign an internal risk view or rating direction.
5. Write monitoring triggers and early warning signals.

• Professional brief
• Financial model or analytical workbook
• Presentation deck
• Source log
• Interview talking points
• Source and assumption log
• One-page executive summary
• Final memo PDF

• Public transaction announcements, DRHP/RHP filings, exchange releases, rating reports, and company presentations.
• Annual report
• Credit rating rationale
• RBI/SEBI filings where relevant
• Sector data
• Company news and lender disclosures

• Clear problem statement and role context
• Source-backed assumptions
• Clean model, memo, notebook, or dashboard
• Decision recommendation with risks
• Resume bullet and interview talk track

• Problem: explain the business question and why it matters for FinTech and Financial Platforms.
• Method: describe the data collected, assumptions made, and analysis performed.
• Decision: state the recommendation, key risk, and what would change your view.

Act as a junior FinTech and Financial Platforms analyst. Use public information to produce a decision-ready work product for a manager or interview panel.

Show that you can apply Finance product management, Unit economics, Machine learning, SQL, Python in a practical analyst workflow, not only explain the theory.

• Define the hypothesis before coding.
• Select universe, date range, frequency, and benchmark.
• Create a reproducible notebook structure: import, clean, signal, test, evaluate, explain.

1. Clean and validate the dataset.
2. Build signals or factors with no look-ahead bias.
3. Run backtest with transaction-cost assumptions.
4. Calculate returns, volatility, drawdown, Sharpe, hit rate, beta, and benchmark comparison.
5. Write a limitations section explaining survivorship, data quality, and overfitting risk.

• Professional brief
• Financial model or analytical workbook
• Presentation deck
• Source log
• Interview talking points
• Source and assumption log
• One-page executive summary
• Final output file

• Public filings, official regulatory material, public datasets, company career/domain research, and a documented source log.
• Yahoo Finance or Stooq price data
• NSE/BSE public data
• FRED or World Bank macro data
• Company fundamentals from public filings
• Synthetic data when licensing blocks redistribution

• Clear problem statement and role context
• Source-backed assumptions
• Clean model, memo, notebook, or dashboard
• Decision recommendation with risks
• Resume bullet and interview talk track

• Problem: explain the business question and why it matters for FinTech and Financial Platforms.
• Method: describe the data collected, assumptions made, and analysis performed.
• Decision: state the recommendation, key risk, and what would change your view.

Act as a junior FinTech and Financial Platforms analyst. Use public information to produce a decision-ready work product for a manager or interview panel.

Show that you can apply Finance product management, Unit economics, Machine learning, SQL, Python in a practical analyst workflow, not only explain the theory.

• Define the hypothesis before coding.
• Select universe, date range, frequency, and benchmark.
• Create a reproducible notebook structure: import, clean, signal, test, evaluate, explain.

1. Clean and validate the dataset.
2. Build signals or factors with no look-ahead bias.
3. Run backtest with transaction-cost assumptions.
4. Calculate returns, volatility, drawdown, Sharpe, hit rate, beta, and benchmark comparison.
5. Write a limitations section explaining survivorship, data quality, and overfitting risk.

• Professional brief
• Financial model or analytical workbook
• Presentation deck
• Source log
• Interview talking points
• Source and assumption log
• One-page executive summary
• Final output file

• Public filings, official regulatory material, public datasets, company career/domain research, and a documented source log.
• Yahoo Finance or Stooq price data
• NSE/BSE public data
• FRED or World Bank macro data
• Company fundamentals from public filings
• Synthetic data when licensing blocks redistribution

• Clear problem statement and role context
• Source-backed assumptions
• Clean model, memo, notebook, or dashboard
• Decision recommendation with risks
• Resume bullet and interview talk track

• Problem: explain the business question and why it matters for FinTech and Financial Platforms.
• Method: describe the data collected, assumptions made, and analysis performed.
• Decision: state the recommendation, key risk, and what would change your view.

Act as a junior FinTech and Financial Platforms analyst. Use public information to produce a decision-ready work product for a manager or interview panel.

Show that you can apply Finance product management, Unit economics, Machine learning, SQL, Python in a practical analyst workflow, not only explain the theory.

• Define the hypothesis before coding.
• Select universe, date range, frequency, and benchmark.
• Create a reproducible notebook structure: import, clean, signal, test, evaluate, explain.

1. Clean and validate the dataset.
2. Build signals or factors with no look-ahead bias.
3. Run backtest with transaction-cost assumptions.
4. Calculate returns, volatility, drawdown, Sharpe, hit rate, beta, and benchmark comparison.
5. Write a limitations section explaining survivorship, data quality, and overfitting risk.

• Professional brief
• Financial model or analytical workbook
• Presentation deck
• Source log
• Interview talking points
• Source and assumption log
• One-page executive summary
• Final output file

• Public filings, official regulatory material, public datasets, company career/domain research, and a documented source log.
• Yahoo Finance or Stooq price data
• NSE/BSE public data
• FRED or World Bank macro data
• Company fundamentals from public filings
• Synthetic data when licensing blocks redistribution

• Clear problem statement and role context
• Source-backed assumptions
• Clean model, memo, notebook, or dashboard
• Decision recommendation with risks
• Resume bullet and interview talk track

• Problem: explain the business question and why it matters for FinTech and Financial Platforms.
• Method: describe the data collected, assumptions made, and analysis performed.
• Decision: state the recommendation, key risk, and what would change your view.

Act as a junior Risk Management and Compliance analyst. Use public information to produce a decision-ready work product for a manager or interview panel.

Show that you can apply Risk modeling, Credit analysis, AML/KYC, Indian financial regulation, Statistics in a practical analyst workflow, not only explain the theory.

• Pick a borrower, issuer, or sector case.
• Collect financials, debt schedule, ratings, business profile, and management commentary.
• Define the lending/rating question and time horizon.

1. Analyze revenue quality, margins, leverage, coverage, liquidity, and working capital.
2. Identify business, financial, management, and industry risks.
3. Run base and stress cases for cash flow and debt servicing.
4. Assign an internal risk view or rating direction.
5. Write monitoring triggers and early warning signals.

• Professional brief
• Financial model or analytical workbook
• Presentation deck
• Source log
• Interview talking points
• Source and assumption log
• One-page executive summary
• Final memo PDF

• Annual reports, investor presentations, exchange filings, SEBI/RBI/NSE/BSE pages, and official company disclosures.
• Annual report
• Credit rating rationale
• RBI/SEBI filings where relevant
• Sector data
• Company news and lender disclosures

• Clear problem statement and role context
• Source-backed assumptions
• Clean model, memo, notebook, or dashboard
• Decision recommendation with risks
• Resume bullet and interview talk track

• Problem: explain the business question and why it matters for Risk Management and Compliance.
• Method: describe the data collected, assumptions made, and analysis performed.
• Decision: state the recommendation, key risk, and what would change your view.

Act as a junior Risk Management and Compliance analyst. Use public information to produce a decision-ready work product for a manager or interview panel.

Show that you can apply Risk modeling, Credit analysis, AML/KYC, Indian financial regulation, Statistics in a practical analyst workflow, not only explain the theory.

• Pick a borrower, issuer, or sector case.
• Collect financials, debt schedule, ratings, business profile, and management commentary.
• Define the lending/rating question and time horizon.

1. Analyze revenue quality, margins, leverage, coverage, liquidity, and working capital.
2. Identify business, financial, management, and industry risks.
3. Run base and stress cases for cash flow and debt servicing.
4. Assign an internal risk view or rating direction.
5. Write monitoring triggers and early warning signals.

• Professional brief
• Financial model or analytical workbook
• Presentation deck
• Source log
• Interview talking points
• Source and assumption log
• One-page executive summary
• Final memo PDF

• Public transaction announcements, DRHP/RHP filings, exchange releases, rating reports, and company presentations.
• Annual report
• Credit rating rationale
• RBI/SEBI filings where relevant
• Sector data
• Company news and lender disclosures

• Clear problem statement and role context
• Source-backed assumptions
• Clean model, memo, notebook, or dashboard
• Decision recommendation with risks
• Resume bullet and interview talk track

• Problem: explain the business question and why it matters for Risk Management and Compliance.
• Method: describe the data collected, assumptions made, and analysis performed.
• Decision: state the recommendation, key risk, and what would change your view.

Act as a junior Risk Management and Compliance analyst. Use public information to produce a decision-ready work product for a manager or interview panel.

Show that you can apply Risk modeling, Credit analysis, AML/KYC, Indian financial regulation, Statistics in a practical analyst workflow, not only explain the theory.

• Pick a borrower, issuer, or sector case.
• Collect financials, debt schedule, ratings, business profile, and management commentary.
• Define the lending/rating question and time horizon.

1. Analyze revenue quality, margins, leverage, coverage, liquidity, and working capital.
2. Identify business, financial, management, and industry risks.
3. Run base and stress cases for cash flow and debt servicing.
4. Assign an internal risk view or rating direction.
5. Write monitoring triggers and early warning signals.

• Professional brief
• Financial model or analytical workbook
• Presentation deck
• Source log
• Interview talking points
• Source and assumption log
• One-page executive summary
• Final memo PDF

• Public filings, official regulatory material, public datasets, company career/domain research, and a documented source log.
• Annual report
• Credit rating rationale
• RBI/SEBI filings where relevant
• Sector data
• Company news and lender disclosures

• Clear problem statement and role context
• Source-backed assumptions
• Clean model, memo, notebook, or dashboard
• Decision recommendation with risks
• Resume bullet and interview talk track

• Problem: explain the business question and why it matters for Risk Management and Compliance.
• Method: describe the data collected, assumptions made, and analysis performed.
• Decision: state the recommendation, key risk, and what would change your view.

Act as a junior Risk Management and Compliance analyst. Use public information to produce a decision-ready work product for a manager or interview panel.

Show that you can apply Risk modeling, Credit analysis, AML/KYC, Indian financial regulation, Statistics in a practical analyst workflow, not only explain the theory.

• Pick a borrower, issuer, or sector case.
• Collect financials, debt schedule, ratings, business profile, and management commentary.
• Define the lending/rating question and time horizon.

1. Analyze revenue quality, margins, leverage, coverage, liquidity, and working capital.
2. Identify business, financial, management, and industry risks.
3. Run base and stress cases for cash flow and debt servicing.
4. Assign an internal risk view or rating direction.
5. Write monitoring triggers and early warning signals.

• Professional brief
• Financial model or analytical workbook
• Presentation deck
• Source log
• Interview talking points
• Source and assumption log
• One-page executive summary
• Final memo PDF

• Public filings, official regulatory material, public datasets, company career/domain research, and a documented source log.
• Annual report
• Credit rating rationale
• RBI/SEBI filings where relevant
• Sector data
• Company news and lender disclosures

• Clear problem statement and role context
• Source-backed assumptions
• Clean model, memo, notebook, or dashboard
• Decision recommendation with risks
• Resume bullet and interview talk track

• Problem: explain the business question and why it matters for Risk Management and Compliance.
• Method: describe the data collected, assumptions made, and analysis performed.
• Decision: state the recommendation, key risk, and what would change your view.

Act as a junior Risk Management and Compliance analyst. Use public information to produce a decision-ready work product for a manager or interview panel.

Show that you can apply Risk modeling, Credit analysis, AML/KYC, Indian financial regulation, Statistics in a practical analyst workflow, not only explain the theory.

• Pick a borrower, issuer, or sector case.
• Collect financials, debt schedule, ratings, business profile, and management commentary.
• Define the lending/rating question and time horizon.

1. Analyze revenue quality, margins, leverage, coverage, liquidity, and working capital.
2. Identify business, financial, management, and industry risks.
3. Run base and stress cases for cash flow and debt servicing.
4. Assign an internal risk view or rating direction.
5. Write monitoring triggers and early warning signals.

• Professional brief
• Financial model or analytical workbook
• Presentation deck
• Source log
• Interview talking points
• Source and assumption log
• One-page executive summary
• Final memo PDF

• Public filings, official regulatory material, public datasets, company career/domain research, and a documented source log.
• Annual report
• Credit rating rationale
• RBI/SEBI filings where relevant
• Sector data
• Company news and lender disclosures

• Clear problem statement and role context
• Source-backed assumptions
• Clean model, memo, notebook, or dashboard
• Decision recommendation with risks
• Resume bullet and interview talk track

• Problem: explain the business question and why it matters for Risk Management and Compliance.
• Method: describe the data collected, assumptions made, and analysis performed.
• Decision: state the recommendation, key risk, and what would change your view.

Act as a junior Credit Rating Agencies analyst. Use public information to produce a decision-ready work product for a manager or interview panel.

Show that you can apply Credit analysis, Financial modeling, Fixed income, Research writing, Indian financial regulation in a practical analyst workflow, not only explain the theory.

• Define the business model and key operating drivers.
• Create clean input, historicals, forecast, output, and checks tabs.
• Separate assumptions from formulas.
• Decide base, upside, and downside scenarios before modeling.

1. Build historical P&L, balance sheet, and cash flow lines.
2. Create drivers for revenue, cost, margin, capex, working capital, and debt.
3. Link the statements and add balance checks.
4. Create scenario and sensitivity outputs.
5. Write a finance-manager style summary with the main movement, risk, and decision required.

• Professional brief
• Financial model or analytical workbook
• Presentation deck
• Source log
• Interview talking points
• Source and assumption log
• One-page executive summary
• Final output file

• Annual reports, investor presentations, exchange filings, SEBI/RBI/NSE/BSE pages, and official company disclosures.
• Company annual report
• Quarterly filings
• Investor presentation
• Public industry data
• Synthetic operating dataset if company data is unavailable

• Clear problem statement and role context
• Source-backed assumptions
• Clean model, memo, notebook, or dashboard
• Decision recommendation with risks
• Resume bullet and interview talk track

• Problem: explain the business question and why it matters for Credit Rating Agencies.
• Method: describe the data collected, assumptions made, and analysis performed.
• Decision: state the recommendation, key risk, and what would change your view.

Act as a junior Credit Rating Agencies analyst. Use public information to produce a decision-ready work product for a manager or interview panel.

Show that you can apply Credit analysis, Financial modeling, Fixed income, Research writing, Indian financial regulation in a practical analyst workflow, not only explain the theory.

• Define the business model and key operating drivers.
• Create clean input, historicals, forecast, output, and checks tabs.
• Separate assumptions from formulas.
• Decide base, upside, and downside scenarios before modeling.

1. Build historical P&L, balance sheet, and cash flow lines.
2. Create drivers for revenue, cost, margin, capex, working capital, and debt.
3. Link the statements and add balance checks.
4. Create scenario and sensitivity outputs.
5. Write a finance-manager style summary with the main movement, risk, and decision required.

• Professional brief
• Financial model or analytical workbook
• Presentation deck
• Source log
• Interview talking points
• Source and assumption log
• One-page executive summary
• Final output file

• Public transaction announcements, DRHP/RHP filings, exchange releases, rating reports, and company presentations.
• Company annual report
• Quarterly filings
• Investor presentation
• Public industry data
• Synthetic operating dataset if company data is unavailable

• Clear problem statement and role context
• Source-backed assumptions
• Clean model, memo, notebook, or dashboard
• Decision recommendation with risks
• Resume bullet and interview talk track

• Problem: explain the business question and why it matters for Credit Rating Agencies.
• Method: describe the data collected, assumptions made, and analysis performed.
• Decision: state the recommendation, key risk, and what would change your view.

Act as a junior Credit Rating Agencies analyst. Use public information to produce a decision-ready work product for a manager or interview panel.

Show that you can apply Credit analysis, Financial modeling, Fixed income, Research writing, Indian financial regulation in a practical analyst workflow, not only explain the theory.

• Define the business model and key operating drivers.
• Create clean input, historicals, forecast, output, and checks tabs.
• Separate assumptions from formulas.
• Decide base, upside, and downside scenarios before modeling.

1. Build historical P&L, balance sheet, and cash flow lines.
2. Create drivers for revenue, cost, margin, capex, working capital, and debt.
3. Link the statements and add balance checks.
4. Create scenario and sensitivity outputs.
5. Write a finance-manager style summary with the main movement, risk, and decision required.

• Professional brief
• Financial model or analytical workbook
• Presentation deck
• Source log
• Interview talking points
• Source and assumption log
• One-page executive summary
• Final output file

• Public filings, official regulatory material, public datasets, company career/domain research, and a documented source log.
• Company annual report
• Quarterly filings
• Investor presentation
• Public industry data
• Synthetic operating dataset if company data is unavailable

• Clear problem statement and role context
• Source-backed assumptions
• Clean model, memo, notebook, or dashboard
• Decision recommendation with risks
• Resume bullet and interview talk track

• Problem: explain the business question and why it matters for Credit Rating Agencies.
• Method: describe the data collected, assumptions made, and analysis performed.
• Decision: state the recommendation, key risk, and what would change your view.

Act as a junior Credit Rating Agencies analyst. Use public information to produce a decision-ready work product for a manager or interview panel.

Show that you can apply Credit analysis, Financial modeling, Fixed income, Research writing, Indian financial regulation in a practical analyst workflow, not only explain the theory.

• Define the business model and key operating drivers.
• Create clean input, historicals, forecast, output, and checks tabs.
• Separate assumptions from formulas.
• Decide base, upside, and downside scenarios before modeling.

1. Build historical P&L, balance sheet, and cash flow lines.
2. Create drivers for revenue, cost, margin, capex, working capital, and debt.
3. Link the statements and add balance checks.
4. Create scenario and sensitivity outputs.
5. Write a finance-manager style summary with the main movement, risk, and decision required.

• Professional brief
• Financial model or analytical workbook
• Presentation deck
• Source log
• Interview talking points
• Source and assumption log
• One-page executive summary
• Final output file

• Public filings, official regulatory material, public datasets, company career/domain research, and a documented source log.
• Company annual report
• Quarterly filings
• Investor presentation
• Public industry data
• Synthetic operating dataset if company data is unavailable

• Clear problem statement and role context
• Source-backed assumptions
• Clean model, memo, notebook, or dashboard
• Decision recommendation with risks
• Resume bullet and interview talk track

• Problem: explain the business question and why it matters for Credit Rating Agencies.
• Method: describe the data collected, assumptions made, and analysis performed.
• Decision: state the recommendation, key risk, and what would change your view.

Act as a junior Credit Rating Agencies analyst. Use public information to produce a decision-ready work product for a manager or interview panel.

Show that you can apply Credit analysis, Financial modeling, Fixed income, Research writing, Indian financial regulation in a practical analyst workflow, not only explain the theory.

• Define the business model and key operating drivers.
• Create clean input, historicals, forecast, output, and checks tabs.
• Separate assumptions from formulas.
• Decide base, upside, and downside scenarios before modeling.

1. Build historical P&L, balance sheet, and cash flow lines.
2. Create drivers for revenue, cost, margin, capex, working capital, and debt.
3. Link the statements and add balance checks.
4. Create scenario and sensitivity outputs.
5. Write a finance-manager style summary with the main movement, risk, and decision required.

• Professional brief
• Financial model or analytical workbook
• Presentation deck
• Source log
• Interview talking points
• Source and assumption log
• One-page executive summary
• Final output file

• Public filings, official regulatory material, public datasets, company career/domain research, and a documented source log.
• Company annual report
• Quarterly filings
• Investor presentation
• Public industry data
• Synthetic operating dataset if company data is unavailable

• Clear problem statement and role context
• Source-backed assumptions
• Clean model, memo, notebook, or dashboard
• Decision recommendation with risks
• Resume bullet and interview talk track

• Problem: explain the business question and why it matters for Credit Rating Agencies.
• Method: describe the data collected, assumptions made, and analysis performed.
• Decision: state the recommendation, key risk, and what would change your view.

Act as a junior Academia, Policy, and Advisory analyst. Use public information to produce a decision-ready work product for a manager or interview panel.

Show that you can apply Research writing, Indian financial regulation, Statistics, Valuation, ESG analysis in a practical analyst workflow, not only explain the theory.

• Pick one listed company and define the valuation date.
• Collect financial statements for at least three years.
• Choose 4-6 comparable companies and capture market multiples.
• Create an assumptions tab with revenue, margin, capex, working capital, WACC, and terminal growth.

1. Build historical financials and clean one-off items.
2. Forecast revenue, costs, taxes, capex, and working capital.
3. Calculate DCF value, trading comps value, and transaction comps value where possible.
4. Run sensitivity on WACC, terminal growth, exit multiple, and margin assumptions.
5. Write a final recommendation explaining upside/downside, key risks, and what would change your view.

• Professional brief
• Financial model or analytical workbook
• Presentation deck
• Source log
• Interview talking points
• Source and assumption log
• One-page executive summary
• Final output file

• Annual reports, investor presentations, exchange filings, SEBI/RBI/NSE/BSE pages, and official company disclosures.
• Latest annual report
• Quarterly results or investor presentation
• NSE/BSE price history
• Peer company filings
• Public transaction/news sources

• Clear problem statement and role context
• Source-backed assumptions
• Clean model, memo, notebook, or dashboard
• Decision recommendation with risks
• Resume bullet and interview talk track

• Problem: explain the business question and why it matters for Academia, Policy, and Advisory.
• Method: describe the data collected, assumptions made, and analysis performed.
• Decision: state the recommendation, key risk, and what would change your view.

Act as a junior Academia, Policy, and Advisory analyst. Use public information to produce a decision-ready work product for a manager or interview panel.

Show that you can apply Research writing, Indian financial regulation, Statistics, Valuation, ESG analysis in a practical analyst workflow, not only explain the theory.

• Pick one listed company and define the valuation date.
• Collect financial statements for at least three years.
• Choose 4-6 comparable companies and capture market multiples.
• Create an assumptions tab with revenue, margin, capex, working capital, WACC, and terminal growth.

1. Build historical financials and clean one-off items.
2. Forecast revenue, costs, taxes, capex, and working capital.
3. Calculate DCF value, trading comps value, and transaction comps value where possible.
4. Run sensitivity on WACC, terminal growth, exit multiple, and margin assumptions.
5. Write a final recommendation explaining upside/downside, key risks, and what would change your view.

• Professional brief
• Financial model or analytical workbook
• Presentation deck
• Source log
• Interview talking points
• Source and assumption log
• One-page executive summary
• Final output file

• Public transaction announcements, DRHP/RHP filings, exchange releases, rating reports, and company presentations.
• Latest annual report
• Quarterly results or investor presentation
• NSE/BSE price history
• Peer company filings
• Public transaction/news sources

• Clear problem statement and role context
• Source-backed assumptions
• Clean model, memo, notebook, or dashboard
• Decision recommendation with risks
• Resume bullet and interview talk track

• Problem: explain the business question and why it matters for Academia, Policy, and Advisory.
• Method: describe the data collected, assumptions made, and analysis performed.
• Decision: state the recommendation, key risk, and what would change your view.

Act as a junior Academia, Policy, and Advisory analyst. Use public information to produce a decision-ready work product for a manager or interview panel.

Show that you can apply Research writing, Indian financial regulation, Statistics, Valuation, ESG analysis in a practical analyst workflow, not only explain the theory.

• Pick one listed company and define the valuation date.
• Collect financial statements for at least three years.
• Choose 4-6 comparable companies and capture market multiples.
• Create an assumptions tab with revenue, margin, capex, working capital, WACC, and terminal growth.

1. Build historical financials and clean one-off items.
2. Forecast revenue, costs, taxes, capex, and working capital.
3. Calculate DCF value, trading comps value, and transaction comps value where possible.
4. Run sensitivity on WACC, terminal growth, exit multiple, and margin assumptions.
5. Write a final recommendation explaining upside/downside, key risks, and what would change your view.

• Professional brief
• Financial model or analytical workbook
• Presentation deck
• Source log
• Interview talking points
• Source and assumption log
• One-page executive summary
• Final output file

• Public filings, official regulatory material, public datasets, company career/domain research, and a documented source log.
• Latest annual report
• Quarterly results or investor presentation
• NSE/BSE price history
• Peer company filings
• Public transaction/news sources

• Clear problem statement and role context
• Source-backed assumptions
• Clean model, memo, notebook, or dashboard
• Decision recommendation with risks
• Resume bullet and interview talk track

• Problem: explain the business question and why it matters for Academia, Policy, and Advisory.
• Method: describe the data collected, assumptions made, and analysis performed.
• Decision: state the recommendation, key risk, and what would change your view.

Act as a junior Academia, Policy, and Advisory analyst. Use public information to produce a decision-ready work product for a manager or interview panel.

Show that you can apply Research writing, Indian financial regulation, Statistics, Valuation, ESG analysis in a practical analyst workflow, not only explain the theory.

• Pick one listed company and define the valuation date.
• Collect financial statements for at least three years.
• Choose 4-6 comparable companies and capture market multiples.
• Create an assumptions tab with revenue, margin, capex, working capital, WACC, and terminal growth.

1. Build historical financials and clean one-off items.
2. Forecast revenue, costs, taxes, capex, and working capital.
3. Calculate DCF value, trading comps value, and transaction comps value where possible.
4. Run sensitivity on WACC, terminal growth, exit multiple, and margin assumptions.
5. Write a final recommendation explaining upside/downside, key risks, and what would change your view.

• Professional brief
• Financial model or analytical workbook
• Presentation deck
• Source log
• Interview talking points
• Source and assumption log
• One-page executive summary
• Final output file

• Public filings, official regulatory material, public datasets, company career/domain research, and a documented source log.
• Latest annual report
• Quarterly results or investor presentation
• NSE/BSE price history
• Peer company filings
• Public transaction/news sources

• Clear problem statement and role context
• Source-backed assumptions
• Clean model, memo, notebook, or dashboard
• Decision recommendation with risks
• Resume bullet and interview talk track

• Problem: explain the business question and why it matters for Academia, Policy, and Advisory.
• Method: describe the data collected, assumptions made, and analysis performed.
• Decision: state the recommendation, key risk, and what would change your view.

Act as a junior Academia, Policy, and Advisory analyst. Use public information to produce a decision-ready work product for a manager or interview panel.

Show that you can apply Research writing, Indian financial regulation, Statistics, Valuation, ESG analysis in a practical analyst workflow, not only explain the theory.

• Pick one listed company and define the valuation date.
• Collect financial statements for at least three years.
• Choose 4-6 comparable companies and capture market multiples.
• Create an assumptions tab with revenue, margin, capex, working capital, WACC, and terminal growth.

1. Build historical financials and clean one-off items.
2. Forecast revenue, costs, taxes, capex, and working capital.
3. Calculate DCF value, trading comps value, and transaction comps value where possible.
4. Run sensitivity on WACC, terminal growth, exit multiple, and margin assumptions.
5. Write a final recommendation explaining upside/downside, key risks, and what would change your view.

• Professional brief
• Financial model or analytical workbook
• Presentation deck
• Source log
• Interview talking points
• Source and assumption log
• One-page executive summary
• Final output file

• Public filings, official regulatory material, public datasets, company career/domain research, and a documented source log.
• Latest annual report
• Quarterly results or investor presentation
• NSE/BSE price history
• Peer company filings
• Public transaction/news sources

• Clear problem statement and role context
• Source-backed assumptions
• Clean model, memo, notebook, or dashboard
• Decision recommendation with risks
• Resume bullet and interview talk track

• Problem: explain the business question and why it matters for Academia, Policy, and Advisory.
• Method: describe the data collected, assumptions made, and analysis performed.
• Decision: state the recommendation, key risk, and what would change your view.

Act as a junior Emerging Finance Roles analyst. Use public information to produce a decision-ready work product for a manager or interview panel.

Show that you can apply ESG analysis, Climate finance, Machine learning, Python, Portfolio construction in a practical analyst workflow, not only explain the theory.

• Pick a borrower, issuer, or sector case.
• Collect financials, debt schedule, ratings, business profile, and management commentary.
• Define the lending/rating question and time horizon.

1. Analyze revenue quality, margins, leverage, coverage, liquidity, and working capital.
2. Identify business, financial, management, and industry risks.
3. Run base and stress cases for cash flow and debt servicing.
4. Assign an internal risk view or rating direction.
5. Write monitoring triggers and early warning signals.

• Professional brief
• Financial model or analytical workbook
• Presentation deck
• Source log
• Interview talking points
• Source and assumption log
• One-page executive summary
• Final memo PDF

• Annual reports, investor presentations, exchange filings, SEBI/RBI/NSE/BSE pages, and official company disclosures.
• Annual report
• Credit rating rationale
• RBI/SEBI filings where relevant
• Sector data
• Company news and lender disclosures

• Clear problem statement and role context
• Source-backed assumptions
• Clean model, memo, notebook, or dashboard
• Decision recommendation with risks
• Resume bullet and interview talk track

• Problem: explain the business question and why it matters for Emerging Finance Roles.
• Method: describe the data collected, assumptions made, and analysis performed.
• Decision: state the recommendation, key risk, and what would change your view.

Act as a junior Emerging Finance Roles analyst. Use public information to produce a decision-ready work product for a manager or interview panel.

Show that you can apply ESG analysis, Climate finance, Machine learning, Python, Portfolio construction in a practical analyst workflow, not only explain the theory.

• Choose a public acquisition or create a realistic buyer-target pair.
• Capture buyer and target financials, share count, debt, cash, and valuation multiples.
• Define transaction structure: cash, stock, debt, or mixed consideration.

1. Build standalone buyer and target earnings.
2. Estimate purchase price, financing cost, share issuance, and synergies.
3. Calculate accretion/dilution under base/upside/downside synergy cases.
4. Explain strategic rationale, integration risk, and valuation fairness.
5. Create a one-page recommendation for whether the buyer should proceed.

• Professional brief
• Financial model or analytical workbook
• Presentation deck
• Source log
• Interview talking points
• Source and assumption log
• One-page executive summary
• Final output file

• Public transaction announcements, DRHP/RHP filings, exchange releases, rating reports, and company presentations.
• Acquirer and target filings
• Deal press release
• Investor presentation
• Exchange announcements
• Public news coverage

• Clear problem statement and role context
• Source-backed assumptions
• Clean model, memo, notebook, or dashboard
• Decision recommendation with risks
• Resume bullet and interview talk track

• Problem: explain the business question and why it matters for Emerging Finance Roles.
• Method: describe the data collected, assumptions made, and analysis performed.
• Decision: state the recommendation, key risk, and what would change your view.

Act as a junior Emerging Finance Roles analyst. Use public information to produce a decision-ready work product for a manager or interview panel.

Show that you can apply ESG analysis, Climate finance, Machine learning, Python, Portfolio construction in a practical analyst workflow, not only explain the theory.

• Define the portfolio, exposure, product, or process under review.
• Choose risk metrics such as VaR, drawdown, volatility, PD/LGD, limits, or loss frequency.
• Set data assumptions and limitations upfront.

1. Clean the dataset and define risk factors.
2. Calculate baseline risk metrics and limit usage.
3. Run stress scenarios and compare against risk appetite.
4. Create a dashboard or memo explaining breaches and trends.
5. Recommend actions such as hedge, reduce, monitor, escalate, or change controls.

• Professional brief
• Financial model or analytical workbook
• Presentation deck
• Source log
• Interview talking points
• Source and assumption log
• One-page executive summary
• Final memo PDF

• Public filings, official regulatory material, public datasets, company career/domain research, and a documented source log.
• NSE/BSE market data
• RBI/SEBI circulars where relevant
• Public historical price data
• Synthetic loss-event dataset
• Company risk disclosures

• Clear problem statement and role context
• Source-backed assumptions
• Clean model, memo, notebook, or dashboard
• Decision recommendation with risks
• Resume bullet and interview talk track

• Problem: explain the business question and why it matters for Emerging Finance Roles.
• Method: describe the data collected, assumptions made, and analysis performed.
• Decision: state the recommendation, key risk, and what would change your view.

Act as a junior Emerging Finance Roles analyst. Use public information to produce a decision-ready work product for a manager or interview panel.

Show that you can apply ESG analysis, Climate finance, Machine learning, Python, Portfolio construction in a practical analyst workflow, not only explain the theory.

• Define the hypothesis before coding.
• Select universe, date range, frequency, and benchmark.
• Create a reproducible notebook structure: import, clean, signal, test, evaluate, explain.

1. Clean and validate the dataset.
2. Build signals or factors with no look-ahead bias.
3. Run backtest with transaction-cost assumptions.
4. Calculate returns, volatility, drawdown, Sharpe, hit rate, beta, and benchmark comparison.
5. Write a limitations section explaining survivorship, data quality, and overfitting risk.

• Professional brief
• Financial model or analytical workbook
• Presentation deck
• Source log
• Interview talking points
• Source and assumption log
• One-page executive summary
• Final output file

• Public filings, official regulatory material, public datasets, company career/domain research, and a documented source log.
• Yahoo Finance or Stooq price data
• NSE/BSE public data
• FRED or World Bank macro data
• Company fundamentals from public filings
• Synthetic data when licensing blocks redistribution

• Clear problem statement and role context
• Source-backed assumptions
• Clean model, memo, notebook, or dashboard
• Decision recommendation with risks
• Resume bullet and interview talk track

• Problem: explain the business question and why it matters for Emerging Finance Roles.
• Method: describe the data collected, assumptions made, and analysis performed.
• Decision: state the recommendation, key risk, and what would change your view.

Act as a junior Emerging Finance Roles analyst. Use public information to produce a decision-ready work product for a manager or interview panel.

Show that you can apply ESG analysis, Climate finance, Machine learning, Python, Portfolio construction in a practical analyst workflow, not only explain the theory.

• Define the hypothesis before coding.
• Select universe, date range, frequency, and benchmark.
• Create a reproducible notebook structure: import, clean, signal, test, evaluate, explain.

1. Clean and validate the dataset.
2. Build signals or factors with no look-ahead bias.
3. Run backtest with transaction-cost assumptions.
4. Calculate returns, volatility, drawdown, Sharpe, hit rate, beta, and benchmark comparison.
5. Write a limitations section explaining survivorship, data quality, and overfitting risk.

• Professional brief
• Financial model or analytical workbook
• Presentation deck
• Source log
• Interview talking points
• Source and assumption log
• One-page executive summary
• Final output file

• Public filings, official regulatory material, public datasets, company career/domain research, and a documented source log.
• Yahoo Finance or Stooq price data
• NSE/BSE public data
• FRED or World Bank macro data
• Company fundamentals from public filings
• Synthetic data when licensing blocks redistribution

• Clear problem statement and role context
• Source-backed assumptions
• Clean model, memo, notebook, or dashboard
• Decision recommendation with risks
• Resume bullet and interview talk track

• Problem: explain the business question and why it matters for Emerging Finance Roles.
• Method: describe the data collected, assumptions made, and analysis performed.
• Decision: state the recommendation, key risk, and what would change your view.