EWS-Data Scientist

Role Description

• 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.

Required Skills & Experience

• 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.