Credit Data Scientist

HIRING: Credit Data Scientist to lead the design, development, and deployment of credit risk models that drive strategic decisions across lending products. In this role, you will apply advanced statistical, econometric, and machine learning techniques to large-scale data in order to minimize credit losses, improve underwriting decisions, and unlock new insights. You will be instrumental in shaping our credit risk architecture while contributing to broader data science efforts that inform product strategy, experimentation, and observability.

Who You Are:

A go-to expert in credit risk with deep expertise across predictive modeling, causal inference, and experimental analysis

Able to seamlessly shift between strategic planning and hands-on data science implementation

Passionate about applying advanced analytics to real-world lending decisions and measurable financial outcomes

Effective at collaborating across disciplines and communicating technical concepts clearly to non-technical audiences

What You’ll Do:

Develop robust credit risk models and deploy them into production environments with measurable improvements in AUC/ROC/KS

Apply statistical, machine learning, and causal inference methods to understand customer behavior, predict future performance, and identify attribution of outcomes

Design, analyze, and interpret experiments (A/B tests, quasi-experiments) to guide product and credit strategy

Build and operationalize metrics frameworks and observability systems to track credit and product performance

Create clear, compelling analyses and data visualizations that influence cross-functional decisions

Partner closely with engineers, product teams, DevOps, and data infrastructure to launch scalable, real-time modeling solutions

Drive the collection of new data sources and improve existing data pipelines to enrich model features and insights

Engage with regulators and capital partners to ensure models and risk processes meet compliance and capital adequacy standards

Qualifications:

6+ years of experience in credit risk modeling or consumer lending analytics, ideally with exposure to high-growth fintech or neobank environments

4+ years of applied data science experience with a strong track record of innovation and thought leadership

Expert in SQL and proficient in scientific programming languages such as Python or R

Deep theoretical and practical knowledge of statistics, machine learning, and experimental design

Experience shipping credit models into production with demonstrable business impact

Master’s or Ph.D. in a quantitative field such as Computer Science, Mathematics, Engineering, Economics, or related discipline from a top-tier institution