# Head of Credit & Underwriting

**Company:** [Atlas](https://hotfix.jobs/companies/atlas)
**Location:** New York, NY, San Francisco, CA
**Role:** Finance & Accounting
**Experience:** 8+ years
**Skills:** SQL, Python, R, Credit Risk, Underwriting, Line Sizing, Loss Forecasting, Cecl Modeling, Credit Facilities, Data Analysis
**Posted:** 2026-06-17

> Own credit decisions, line sizing, underwriting systems, and portfolio risk management for a high-net-worth charge card. Requires 8+ years credit risk experience, SQL/Python proficiency, and hands-on model-building in fintech or major issuers.

## Job Description

## What you'll actually do
- Own line sizing end to end. Build dynamic limits that expand with payment behavior and cash flow, not static tiers.
- Own the application and onboarding pipeline. Work directly with engineering to cut time-to-decision.
- Build the Atlas Business underwriting engine. Entity-based decisioning from Secretary of State records, business credit bureaus, bank data, and industry risk.
- Manage the credit facility. Partner with the VP of Finance on warehouse operations, covenant compliance, draw optimization, and lender reporting.
- Own collections and loss mitigation. Build the early-warning systems, contact strategies, and recovery processes.
- Build loss forecasting and CECL models.
- Monitor the portfolio obsessively. Own the dashboards for risk visibility including concentration, delinquency aging, cohort and vintage performance.

## What you bring
- 8+ years in credit risk, ideally starting at a major issuer (Amex, Chase, Capital One, Citi, Discover) and then moving into an operating role at a high-growth fintech.
- Write SQL and comfortable in Python or R. Built models, not just reviewed them.
- Understand charge card economics specifically, not just revolving credit.
- Worked with credit warehouse facilities: advance rates, eligibility, covenants, lender reporting.
- Underwritten high-net-worth or affluent segments.
- Built infrastructure at a startup. Comfortable with ambiguity, speed, and deciding with imperfect information.
- Bachelor's in a quantitative field (statistics, economics, math, engineering, CS) or equivalent depth of experience.

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