Job Responsibilities:

- Design, develop, and support production ML solutions for Trade Surveillance and/or Financial Crime use cases (e.g., 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, backtesting, stability/drift analysis, and support for reviews with Model Risk / Audit / Controls partners (as applicable).
- Partner with Technology/MLOps to support CI/CD processes, model versioning/registries, and automated monitoring (data drift, performance) for reliable operation in production.
- Work with FCC / Surveillance SMEs, investigators/reviewers, and Operations to translate typologies/red flags into defensible ML controls; incorporate human-in-the-loop feedback to improve model usability and precision.
- Deliver interpretable outputs for 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 s in a quantitative discipline (Computer Science, Statistics, Mathematics, Economics, Operations Research, or related).
- Minimum 3 years of hands-on applied ML / data science experience; exposure to Financial Crime (AML/sanctions/fraud) and/or Trade Surveillance is preferred.
- Strong Python and ML tooling (e.g., pandas, scikit-learn; Spark/PySpark a plus).
- Working knowledge of imbalanced learning and operational evaluation (precision/recall, PR-AUC, alert yield) and threshold optimization/calibration .
- Experience supporting model governance expectations: 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 transparent reason codes, and collaborate effectively with Compliance/Surveillance, Ops, and Technology stakeholders.

Disclaimer : This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.

📌 Senior Associate - Data Science/Applied AI ML (Mumbai)
🏢 JPMorgan Chase Bank
📍 Mumbai