Atlas's mission is "Credit Made Easy" expanding access to credit for the roughly 80% of applicants without an existing credit history who are turned away by traditional card issuers. The Atlas Rewards Mastercard closes that gap directly: 0% APR, no credit history required, and spending limits that grow with the member. Beyond the card, Atlas offers a deposit account with early paycheck access, credit score tracking, a subscription manager, a tax filing service, and cash back at more than 50,000 locations.
Atlas operates as a small, intentional team that would rather leave a seat open than fill it with the wrong person. The company runs on high trust and high ownership — no bureaucracy, no approval chains, people who own outcomes end to end — practices radical transparency, giving everyone full context on financials, strategy, and metrics to make good decisions, and pays for impact rather than activity or tenure.
What You'll DoOwn portfolio performance. Build and maintain the core view of the book: delinquency and loss curves, vintages, roll rates, and performance by segment. Spot problems early and explain what's driving them.
Own limits and exposure. Track credit limit history, line increases, and growth in unsecured exposure. Decide whether the limit strategy is earning its risk, and recommend where to expand or pull back.
Shape credit policy. Evaluate cutoffs, eligibility rules, and limit assignment. Size the approval, loss, and exposure impact of policy changes before they ship, then measure what actually happened after.
Define how credit is measured. Set the bad definitions, performance windows, and evaluation sets that every policy and model decision is judged against.
Partner with credit engineering on the models. Slice model performance by segment, find where predictions break down, and turn those gaps into specific asks — a new feature, data source, or signal. Help design the tests that show whether a change worked.
Monitor what's in production. Track calibration, drift, and stability of models and policies, and build the recurring credit reporting the company relies on.
Support compliance. Contribute to fair lending testing, adverse action reason quality, and credit documentation.
3+ years in credit risk, credit strategy, underwriting analytics, or a closely related analytical role. Strong analysts from adjacent fields (quant finance, fraud, insurance, economics) who can learn credit fast are welcome.
Solid credit intuition, or a clear ability to build it quickly.
Strong SQL and Python, self-sufficient in Atlas's warehouse (BigQuery).
Active use of AI tools to write queries, explore data, and speed up analysis, or eagerness to learn fast, paired with judgment about when to check AI output, since a confident wrong answer is expensive in credit.
Comfort with ambiguity and with working on a problem nobody has fully defined yet. This role is new, and the person in it will help shape it.
A fast learner, valued over already knowing every technique the role touches.
Clear communication, a precise ask for an engineer and a crisp recommendation for a founder, often about the same analysis.
Nice-to-Haves
Experience at a card issuer, lender, or lending fintech.
Experience with cash-flow or bank transaction data, such as Plaid or other open banking sources.
Familiarity evaluating ML underwriting models: calibration, segment performance, SHAP or other attribution methods.
Exposure to transformer or embedding-based risk models.
Background in model validation or model risk management.
This role sits at the center of what Atlas is building: extending real, usable credit to people the traditional system turns away, without loosening the discipline that keeps that credit sound. The credit book is one of the company's most closely watched surfaces, and this role owns the view of it. Delinquency, loss, exposure, and policy performance, that the founders and credit engineering rely on to know how the book is doing and what should change. It's a newly created role with real latitude to define how credit performance gets measured at Atlas, working directly alongside the engineers building the underwriting models rather than downstream of them.
Benefits & PerksCompetitive salary with market-leading equity
Medical, dental, and vision insurance with 401(k) match
Unlimited PTO
Office lunch and dinner provided
A small, high-ownership team with direct access to the founders and full context on company strategy and performance
$120,000-$180,000
Skills Required
- 3+ years of experience in credit risk, credit strategy, underwriting analytics, or a closely related analytical role
- Strong credit intuition or the ability to develop it quickly
- Strong SQL skills
- Strong Python skills
- Ability to work independently in BigQuery
- Active use of AI tools for querying, data exploration, and analysis, or eagerness to learn quickly
- Judgment to validate AI-generated outputs
- Comfort with ambiguity and undefined problems
- Fast learning ability
- Clear communication and ability to provide precise technical asks and recommendations
- Experience at a card issuer, lender, or lending fintech
- Experience with cash-flow or bank transaction data, including Plaid or similar open banking sources
- Familiarity with evaluating machine-learning underwriting models, including calibration, segment performance, and SHAP or other attribution methods
- Exposure to transformer- or embedding-based risk models
- Background in model validation or model risk management
What We Do
Atlas is a financial technology company building a modern rewards credit card and banking platform with FDIC-insured bank partners. It focuses on people underserved by the financial system, offering access to credit and rewards while helping members build credit through a cash-flow-based approach. Its membership bundles card and banking features with savings automation, credit monitoring, and subscription management, aiming to make financial progress less stressful and more accessible.
