Location: 100% Remote (Fully Asynchronous)
Job Type: Full-Time Contract (40 hours/week)
About the Opportunity
We are partnering with a venture-backed artificial intelligence company based in San Francisco developing frontier AI systems specialized in institutional financial reasoning and quantitative analysis.
The goal is to teach AI how seasoned credit investors, lenders, and debt structurers analyze risk, model capital structures, and make underwriting decisions. Rather than relying on simple debt templates, the platform evaluates AI systems on their ability to build and audit dynamic debt mechanics from the ground up.
In this role, you will serve as a Credit & Debt subject-matter expert. Working entirely from a blank spreadsheet in Excel, you will build, stress-test, and document institutional-grade credit models. Your completed workbooks, formulas, and written step-by-step logic will serve as the verifiable ground-truth datasets used to train, benchmark, and fine-tune AI models against institutional lending standards.
Key Responsibilities
Blank-Sheet Debt Modeling: Construct integrated credit, debt sizing, and repayment models completely from scratch in Excel without relying on pre-built templates, plug-ins, or automation tools.
Complex Debt Schedules & Sculpting: Build dynamic multi-tranche debt schedules (senior secured, mezzanine/subordinated, revolving facilities, unitranche, PIK notes) including mandatory amortization, cash sweeps, interest capitalization, and prepayment mechanics.
Cash Flow Available for Debt Service (CFADS): Model and stress-test core coverage and leverage metrics, including DSCR, LLCR, PLCR, interest coverage, and net debt-to-EBITDA ratios across varying business cycles.
Covenant & Downside Stress-Testing: Construct multi-variable sensitivity tables and downside scenarios to evaluate default probabilities, covenant headroom, breakeven cash flows, and liquidation/recovery outcomes.
Structural Credit Logic: Build collateral and borrowing base mechanisms (e.g., eligible receivables/inventory, advance rates, concentration limits, and reserves).
Ground-Truth Logic Documentation: Provide clear, structured written explanations in English detailing the credit rationale, structural protections, formula mechanics, and accounting relationships embedded in each model.
Must-Have Requirements
2–8+ years of direct experience in Private Debt, Credit Funds, Leveraged Finance, Direct Lending, Project Finance, Rating Agencies, or Credit Risk / Underwriting.
Demonstrated "From-Scratch" Excel Mastery: Proven ability to build full, auditable debt schedules and cash flow models from a blank workbook using disciplined financial modeling standards (e.g., FAST or institutional credit equivalents with zero circularity errors).
Advanced Credit Mechanics: Hands-on experience structuring cash flow waterfalls, principal repayment schedules, covenant compliance checks, and reserve accounts (e.g., DSRA).
Deep Financial Accounting Knowledge: Solid command of 3-statement financial statements (US GAAP / IFRS) and how working capital swings, Capex, and operational shocks flow directly into cash available for debt service.
Full-Time Dedication: Availability to commit 35–40 dedicated hours per week on an ongoing basis.
Written Communication: Fluent professional English with the ability to clearly articulate credit risk logic, covenants, and mathematical steps in writing.
Nice-to-Have Qualifications
Asset-Based Lending (ABL): Experience modeling borrowing bases, advance rates, dilution, and collateral-driven credit facilities.
Structured Finance / ABS: Experience with tranched waterfalls, loss distributions, credit enhancements, and prepayment/default curves.
Why Join?
Shape AI in Credit Analysis: Directly influence how frontier AI models understand risk, debt structures, and credit underwriting.
True Autonomy: 100% remote and fully asynchronous—structure your working hours around output milestones.
Competitive Global Pay: Consistent monthly contractor compensation reflecting your depth of technical credit modeling and weekly output.