Senior Associate Data ScienceApplied AI ML

Job Responsibilities:

• Design develop and supportproduction ML solutionsforTrade Surveillance and/or Financial Crimeuse cases (e.g. alert generation prioritization/triage risk scoring) with focus on measurable risk mitigation and control effectiveness.
• Applysupervised and unsupervised/semi-supervisedmethods (classification anomaly detection clustering; basic weak-supervision/heuristics where needed) to improve true-positive rates and reduce false positives.
• Execute themodel lifecycleunder 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 tomodel governance and risk managementdeliverables: documentation test results backtesting stability/drift analysis and support for reviews with Model Risk / Audit / Controls partners (as applicable).
• Partner withTechnology/MLOpsto support CI/CD processes model versioning/registries and automated monitoring (data drift performance) for reliable operation in production.
• Work withFCC / Surveillance SMEs investigators/reviewers and Operationsto translate typologies/red flags into defensible ML controls; incorporate human-in-the-loop feedback to improve model usability and precision.
• Deliverinterpretable outputsfor end users: reason codes and explainability (e.g. SHAP/LIME-style drivers; simple counterfactual insights where appropriate) to support consistent alert dispositioning.
• Develop solution using GenAI/LLMs(e.g. summarizing narratives extracting signals from unstructured text) as a complement to core statistical/graph ML detection methods.

Required Qualifications Capabilities and Skills:

• Mastersin a quantitative discipline (Computer Science Statistics Mathematics Economics Operations Research or related).
• Minimum 3 yearsof hands-on applied ML / data science experience; exposure toFinancial Crime (AML/sanctions/fraud)and/orTrade Surveillanceis preferred.
• StrongPythonand ML tooling (e.g. pandas scikit-learn; Spark/PySpark a plus).
• Working knowledge ofimbalanced learningand operational evaluation (precision/recall PR-AUC alert yield) andthreshold optimization/calibration.
• Experience supportingmodel governanceexpectations: clear documentation validation testing benchmarking/baselines back testing concepts drift/stability monitoring and explainability suitable for review.
• Strong communication skills to explain models and trade-offs produce clear reason codes and collaborate effectively with Compliance/Surveillance Ops and Technology stakeholders.