Senior Associate - Data Science/Applied 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:

• Master'sin 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.