Lead Data Scientist, Credit Risk

About the Role We are looking for an experienced Credit Risk Data Scientist who is data-driven, has strong analytical and technical skills, and operates with a high degree of ownership. In this role, you will lead the credit strategy that lets Chime lend to people before they have a relationship with us — starting with Instant Loans for new members (top of funnel) and expanding to future unsecured credit products such as credit cards. You'll develop risk policies and strategies to manage credit risk throughout the member lifecycle, and you'll work as a trusted cross-functional partner with product, growth, data engineering, machine learning, and analytics teams, interacting with business stakeholders across many departments. In this role, you can expect to Own credit strategy for new-to-Chime applicants across the member lifecycle, starting with Instant Loans for prospects (top of funnel) and extending to future unsecured products such as credit cards and/or lines of credit Develop and evolve channel-specific credit policies (pre-approval / prescreen, direct mail, open application, referrals, and affiliates) that support growth while balancing risk, unit economics, and member experience Design and continuously iterate acquisition, loan amount / line assignment, pricing, line management, repayment, and collections strategies Leverage credit bureau (e.g., Experian), alternative, and internal Chime data to underwrite applicants with no prior Chime relationship; build credit models and policies hands-on and in partnership with our DSML team Lead test-and-learn: design and read out champion/challenger tests and controlled ramps, and quantify trade-offs among approvals, losses, direct deposit conversion, and lifetime value Partner with Finance and Strategy to size new channels and forecast approval funnels, vintage losses, and unit economics; set launch criteria, guardrails, and expansion triggers Collaborate with data teams to build data infrastructure and early-read monitoring and dashboards (first payment default, delinquency, vintage and roll rates) that help senior management understand credit risk trends and exposures Work with cross-functional teams including DSML, Product, Design, Engineering, and Decision Platform to test and implement risk models and strategies in production, including accurate adverse action reasons Partner with Compliance, Legal, Fair Lending, Model Risk Management, Fraud, and our bank partners to document, approve, and govern credit policies Mentor and work collaboratively with other credit risk data scientists to develop strategies, share knowledge, and raise the team's analytical bar To thrive in this role, you have A Master's degree (required) in Statistics, Economics, Engineering, Math, Finance, Computer Science, or another quantitative discipline; a PhD is a plus 7+ years of analytical or data science experience in Financial Services, preferably at a FinTech company 4+ years of experience building and managing risk for unsecured credit products such as installment loans, lines of credit, or credit cards; acquisition / underwriting experience strongly preferred Hands-on experience with credit bureau data and prescreen, pre-approval, or direct mail credit programs Experience with credit furnishing, bankruptcies, delinquency, and write-off policies, and working knowledge of consumer lending regulation (FCRA, ECOA / Reg B, fair lending, adverse action) Excellent SQL and Python skills, advanced Microsoft Excel skills, and experience working in Looker or a similar business intelligence tool A strong grounding in statistics and machine learning for credit, including scorecards / gradient-boosted models, swap-set analysis, vintage analysis, and experiment design Hands-on, detail-oriented leader with exceptional communication skills who can explain a trade-off to executives and defend the math behind it Ability to work in a fast-paced, complex environment