Machine Learning Engineer, Underwriting

5+ years applying AI, machine learning, or statistical modeling in decisioning contexts such as credit, risk, fraud, recommendations, or similar domains.

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.

You will be a senior individual contributor building and evolving the ML systems behind these products.

You will work across the full modeling lifecycle: problem formulation, feature development, training, calibration, experimentation, deployment, monitoring, and iteration.

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.