Design, develop, and support production ML solutions for Trade Surveillance and/or Financial Crime use cases (., alert generation, prioritization/triage, risk scoring), with focus on measurable risk mitigation and control effectiveness.
Apply supervised and unsupervised/semi-supervised methods (classification, anomaly detection, clustering; basic weak-supervision/heuristics where needed) to improve true-positive rates and reduce false positives.
Execute the model lifecycle under guidance: problem framing, data sourcing and quality checks, feature engineering (behavioral/temporal and basic entity-relationship/graph features), model training, validation, calibration/thresholding, bias/fairness checks, monitoring, and refresh/retraining support.
Contribute to model governance and risk management deliverables: documentation, test results, back testing, stability/drift analysis, and s...
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