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 clear reason codes, and collaborate effectively with Compliance/Surveillance, Ops, and Technology stakeholders.

JPMorganChase, one of the oldest financial institutions, offers innovative financial solutions to millions of consumers, small businesses and many of the world’s most prominent corporate, institutional and government clients under the J.P. Morgan and Chase brands. Our history spans over 200 years and today we are a leader in investment banking, consumer and small business banking, commercial banking, financial transaction processing and asset management.

We recognize that our people are our strength and the diverse talents they bring to our global workforce are directly linked to our success. We are an equal opportunity employer and place a high value on diversity and inclusion at our company. We do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law. We also make reasonable accommodations for applicants’ and employees’ religious practices and beliefs, as well as mental health or physical disability needs. Visit our FAQs for more information about requesting an accommodation.