Senior Associate - Data Science/applied AI ML

JPMorgan Chase · Banking · Mumbai, Maharashtra, India · Corporate Sector

Senior Associate role focused on designing, developing, and supporting production ML solutions for Trade Surveillance and Financial Crime use cases at JPMorgan Chase. The role involves the full model lifecycle, from data sourcing to monitoring and retraining, with a strong emphasis on model governance, risk management, and delivering interpretable outputs. It also includes developing solutions using GenAI/LLMs to complement core ML detection methods.

What you'd actually do

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).
Develop solution using GenAI/LLMs (e.g., summarizing narratives, extracting signals from unstructured text) as a complement to core statistical/graph ML detection methods.

Skills

Required

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
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.

Nice to have

exposure to Financial Crime (AML/sanctions/fraud) and/or Trade Surveillance is preferred.
Spark/PySpark a plus

What the JD emphasized

production ML solutions
model lifecycle
model governance
risk management
GenAI/LLMs

Other signals

production ML solutions
Trade Surveillance and/or Financial Crime
model lifecycle
model governance and risk management
GenAI/LLMs

Read full job 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; 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.

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.