Principal/Senior Research Scientist, Causal and Explainable Credit Risk

ABOUT THE ROLE This role leads the causal inference and explainability agenda within a newly formed research group building the next generation of consumer credit scoring from the ground up. You will bring deep credit-risk domain expertise to ensure models can answer counterfactual questions and produce defensible, regulator-ready explanations. The work sits at the intersection of rigorous academic research and direct real-world impact on consumer lending. WHAT YOU'LL DO - Lead research on causal inference and counterfactual explanations, including deriving actionable reason codes from representation-learning models and estimating treatment effects of consumer actions on future risk. - Design comprehensive evaluation frameworks covering discrimination, calibration, economic-regime stability, fairness analysis, and reason-code accuracy. - Build and own credit-risk modeling components including default prediction, recovery and loss-given-default models, and account-level hazard models. - Translate regulatory and model-governance requirements into concrete architectural and training constraints for foundation model work. - Author methodology documentation for lender model risk teams and regulators, and present findings to those audiences directly. WHAT WE'RE LOOKING FOR - 10 or more years of experience building credit-risk models in production at a bank, card issuer, or advanced fintech lender. - Hands-on applied causal inference experience: double ML, uplift modeling, CATE estimation, instrumental variables, staggered difference-in-differences, or synthetic control methods on real business decisions. - Direct experience designing explainability for regulated credit decisions, including adverse action reason codes, SHAP-based attribution, and counterfactual explanation methods. - Working knowledge of FCRA, ECOA, Regulation B, and model risk management frameworks as applied to credit modeling. - Experience building default, recovery, LGD, or account-level hazard and survival models. - Ability to articulate identification assumptions in causal models and diagnose where they break down. - Experience applying transformer or sequence deep learning architectures (in Python and PyTorch) to credit or transaction data. - A quantitative PhD in mathematics, economics, statistics, or a related empirical-methods field is preferred. - Experience presenting methodology and research findings to regulatory, central-bank, or academic audiences is a strong plus. LOCATION On-site. Primary location is San Francisco, CA. New York, NY and Washington, DC are also accepted locations.