Assist in defining model objectives and decision use, including population, targets, exclusions, and observation/performance windows.
Prepare and integrate modeling datasets from approved on-us and third-party sources; perform data cleaning, transformation, and feature engineering under guidance.
Develop, tune, and test machine learning models for underwriting, collections, portfolio risk, and income estimation, ensuring predictive accuracy and stability.
Evaluate model performance across development, out-of-sample, and out-of-time datasets using standard risk metrics (e.g., KS, AUC, PSI).
Support model explainability and contribute to documentation aligned with MRM standards.
Assist in implementation, monitoring, and retraining of models, including drift detection and performance tracking.
Present findings and insights to technical and non-technical stakeholders, supporting timely and high-quality delivery.
Collaborate effectively with team members, contributing to project management and stakeholder engagement as required.
