- Develop and deploy statistical and machine learning models to predict credit risk, delinquency, defaults, and loan losses.
- Analyze credit bureau, alternative, and loan performance data to improve underwriting, risk segmentation, and credit decisioning.
- Build quantitative pricing models to optimize risk-adjusted returns, loan performance, and conversion.
- Develop and evaluate AI-powered agents and workflows for underwriting, pricing, and analytical operations.
- Apply statistical methods, feature engineering, and model validation techniques to improve predictive accuracy and reliability.
- Partner with Engineering to deploy scalable, production-ready models and analytical pipelines using Python and other data science tools.
- Monitor model performance, investigate data anomalies, and implement improvements based on portfolio trends and realized outcomes.
- Document modeling methodologies and incorporate model governance, fair lending, data privacy, and responsible AI considerations.
- Lead complex data science initiatives, mentor colleagues, and provide feedback on analytical approaches and technical implementations.
- Collaborate with Credit Risk, Product, Engineering, and Finance to translate business challenges into actionable insights and measurable outcomes.
Python