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.