Description
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; straightforward 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 hand

📌 Senior Associate Mumbai
🏢 JPMorganChase
📍 Mumbai