Financial Modeler

Bullpen Talent; India; On-site; INR 1,200,000 - 1,800,000; Part time

Job summary

Bullpen Talent is seeking a Contract Financial Modeler to build detailed financial models in Excel from scratch. This role focuses on structuring assumptions and drivers, aiming for high accuracy. Candidates should have 2–10 years of finance experience with strong Excel skills and the ability to explain financial logic. Flexibility in hours and asynchronous work is provided to accommodate various schedules. A 3-hour take-home modeling exercise will be part of the interview process.

Qualifications

• 2–10 years of experience in finance roles with heavy modeling exposure.
• Proven ability to build models from a blank spreadsheet.
• Strong understanding of how models are used in real financial decisions.

Responsibilities

• Build financial models from scratch in Excel across different use cases.
• Review and debug models built by others.
• Clearly explain financial logic and decision-making.

Skills

Excel modeling

Financial decision-making

Attention to detail

Strong communication skills

Job description

Role: Contract Financial Modeler (Excel)
Hours: 15–40 hours/week · Flexible, asynchronous work

Overview

We are partnering with a U.S.-based technology company building advanced AI systems focused on financial modeling and decision-making.

The goal is to train AI to think like real financial professionals — how models are built from scratch, how assumptions are structured, and how decisions are made using financial models.

This is a hands‑on Excel modeling role. You will be expected to build detailed financial models from a blank spreadsheet and clearly explain your logic.

We prioritize strong model builders over specific domain experience. Candidates from investment banking, private equity, credit, or corporate finance are encouraged to apply if they have built full models from scratch.

What This Role Is (and Is Not)

This role IS:

• Building financial models from a blank spreadsheet
• Structuring assumptions, drivers, and logic
• Linking full model flows (cash flow, debt, returns, scenarios)
• Debugging and improving model accuracy
• Explaining why a model works, not just producing outputs

This role is NOT:

• Dashboards, reporting, or data visualization
• FP&A or budgeting-focused roles
• Reviewing credit without building models
• High‑level strategy without hands‑on modeling
• Using AI tools to generate models

What Good Looks Like

Strong candidates have built full financial models end-to-end (e.g., LBO, 3‑statement, credit, or project finance models) and can clearly explain every component of the model.

Areas of Modeling Experience

(1) Credit & Lending Models

• 3‑statement borrower models
• Cash flow‑based lending and debt sizing
• Covenant modeling (DSCR, leverage, coverage ratios)
• Loan structuring and scenario analysis

(2) Private Equity (LBO Modeling)

• Full LBO models built from scratch
• Sources & uses, debt schedules, returns (IRR/MOIC)
• Operating models with sensitivities and exit scenarios

(3) Asset‑Based Lending (ABL) (optional / less common)

• Borrowing base models (AR, inventory)
• Advance rates, eligibility, dilution
• Collateral‑driven lending structures

(4) Structured Finance / Asset‑Backed Securities (ABS) (optional / less common)

• Cash flow waterfall models
• Tranching, credit enhancement, loss assumptions
• Prepayment/default scenario modeling

(5) Risk & Portfolio Modeling

• Scenario and stress‑testing models
• Loss modeling (default, recovery, exposure)
• Portfolio‑level analysis driven by model outputs

Note: Candidates with strong experience in LBO or credit modeling are highly relevant, even without direct ABL or structured finance experience.

What You’ll Do

• Build financial models from scratch in Excel across different use cases
• Review and debug models built by others
• Identify incorrect assumptions, broken logic, and risks
• Analyze relationships across cash flow, debt, and returns
• Clearly explain financial logic and decision‑making
• Translate real‑world financial thinking into structured outputs

What Makes a Strong Candidate

• 2–10 years of experience in finance roles with heavy modeling exposure
• Proven ability to build models from a blank spreadsheet (no templates)
• Experience building decision‑driving models (not just outputs or summaries)
• Strong understanding of how models are used in real financial decisions
• High attention to detail and model accuracy
• Strong communication skills (English required)

Not a Fit If

Your experience is primarily:

• Investment banking or corporate finance focused on pitch decks, comps, or outputs without building full models
• FP&A reporting, budgeting, or dashboards
• Credit review without hands‑on model building
• BI tools, analytics workflows, or reporting automation

Or:

• You have not built full financial models yourself
• You rely heavily on templates, tools, or AI to generate models
• You are not comfortable explaining your work in detail

Interview Process

All candidates will complete a ~3‑hour take‑home Excel modeling exercise.

• Designed to reflect real‑world modeling work
• Must be completed independently in Excel
• Candidates who are hired will be paid for this exercise

Final Note

If you are a strong financial modeler who has built complex models from scratch and can clearly explain your work, we encourage you to apply — regardless of your specific domain background.