Machine Learning Engineer, Underwriting

What You’ll Do

• Build, evaluate, and maintain underwriting and decisioning models.
• Design and evolve underwriting decision frameworks, including the modeling, automation, policy logic and amount assignment that manage exposure over time.
• Design and run experiments to evaluate model performance, measure impact on approval rates and loss, margin and inform underwriting policy decisions.
• Develop deep understanding of consumer behavior, repayment dynamics, and portfolio structure, and use that to inform model design and decision logic.
• Contribute analysis and perspective that inform portfolio-level decisions, including explaining model behavior, tradeoffs, and uncertainty to senior technical and business leaders.
• Develop and maintain the key portfolio KPIs and inventory of periodic analysis to continuously identify risk and growth opportunities.
• Collaborate with Product, Engineering, Legal, Compliance, and Operations to ensure underwriting systems reflect business goals and regulatory expectations.

Who You Are

• A Master degree in a quantitative field (e.g., Mathematics, Statistics, Physics, Computer Science, Operation Research).
• A PhD degree is strongly welcomed.
• 5+ years applying AI, machine learning, or statistical modeling in decisioning contexts such as credit, risk, fraud, recommendations, or similar domains.
• Experience with probabilistic models and decision systems, including calibration, score transformations, and interpretation of model outputs.
• Strong experimentation skills: you know how to design holdouts, measure lift, and evaluate models beyond aggregate metrics.
• Experience with model monitoring, degradation detection, and retraining strategies in production systems.
• Deep knowledge of underwriting using bank & cashflow analysis, bureau & alternative data etc. with a focus on unsecured credit risk.
• Experience explaining modeling concepts, results, and limitations to senior stakeholders and cross-functional partners.

Bonus Points

• Fintech background
• Consumer finance experience (non-large bank environment)
• Advanced modeling techniques
• Background in small to medium sized companies