EWS-Data Scientist

•Design, build, and document Early Warning System (EWS) / behavioral scorecards (retail and corporate/wholesale) using machine learning techniques such as XGBoost, logistic regression, and random forest.

•Perform end-to-end model development: sample design, target definition (e.g. SMA/NPA flagging), feature selection and hypothesis documentation, hyperparameter tuning (e.g. Optuna), cross-validation (k-fold/OOF), and threshold optimization.

•Conduct independent model validation and technical review of vendor-built or in-house models — including checking implementation code against validation reports, verifying variable weights/coefficients, and identifying documentation or governance gaps.

•Monitor live/production scorecards through Out-of-Time (OOT) validation, tracking discriminatory power (Gini, AUROC), score stability, and population drift; flag and investigate anomalies (e.g. OOT Gini exceeding development Gini).

•Prepare Model Validation Documents, Variable Hypothesis reports, and governance checklists in line with AI/ML Model Risk Management (MRM) frameworks.

•Prepare Model Monitoring reports on a quarterly basis.

•Liaise with bank IT teams to translate model logic into implementation-ready specifications

•Present model findings, enhancements, and validation outcomes to Retail Risk, Collections, and other stakeholders at development and each reporting cycle.

•Strong grounding in machine learning fundamentals (classification techniques, cross-validation, hyperparameter tuning, evaluation metrics such as Gini/AUROC/KS).

•Proficiency in Python or SAS for model development and data analysis (Python preferred; pandas, scikit-learn, XGBoost, Optuna or equivalent).

•Hands-on experience in credit risk analytics, specifically scorecard modelling (behavioral/EWS), model validation, and ongoing portfolio monitoring.

•Working knowledge of SQL for data extraction and variable sourcing from banking systems.

•Experience preparing formal model documentation — validation reports, variable hypothesis documents, governance/AI-ML checklists — suitable for regulatory/MRM review.

•Ability to independently audit a model's implementation (code) against its stated methodology and flag discrepancies.

•Strong written and verbal communication skills for stakeholder presentations and technical write-ups.

•Experience working directly with bank Risk, Collections, and IT teams in a consulting capacity.