Senior Associate - Data Science / Applied AI ML

JPMorgan Chase & Co.; Hyderabad; On-site; INR 1,800,000 - 3,000,000; Full time

Job summary

JPMorgan Chase & Co. seeks an experienced AI/ML specialist to deliver production solutions for conduct risk and regulatory compliance. You will translate complex typologies and control objectives into measurable model outcomes and lead research in supervised, unsupervised, and graph-based methods.

The role emphasizes interpretable ML, human-in-the-loop feedback with investigators, and scalable deployment through CI/CD and model monitoring. Collaboration with RCC and tech teams is essential.

Qualifications

• Master’s degree (PhD preferred) in a quantitative discipline.
• 4+ years hands-on AI/ML experience in regulated domains.
• Experience building or deploying ML solutions with measurable outcomes.
• Strong Python skills and ML frameworks.
• Knowledge of MRM expectations in financial services.
• Clear communication to technical and non-technical stakeholders.
• Mentor juniors via code reviews.

Responsibilities

• Deliver production AI/ML solutions for CCOR risk and compliance use cases.
• Research and innovate in supervised/unsupervised learning, graph analytics, anomaly detection, and weak supervision.
• Develop detection models using supervised/unsupervised/semi-supervised approaches and graph/network analytics.
• Execute model lifecycle: data sourcing, feature engineering, model training, evaluation, calibration, and monitoring.
• Implement interpretable ML and human-in-the-loop workflows with SHAP/LIME and feedback loops.
• Contribute to MLOps and scalable deployment with CI/CD, model registry, and monitoring.
• Support model risk management deliverables with validation and drift analysis.
• Collaborate across RCC, Investigations, Operations, Technology.

Skills

Python

PyTorch

TensorFlow

NLP/LLM

ML deployment

Data analysis

Explainability

Stakeholder comms

Education

Master’s degree or PhD

Quantitative discipline

Tools

Git

CI/CD

SHAP

Model monitoring

Job description

Job Responsibilities:

• Deliver production AI/ML solutionsfor CCOR Conduct risk & compliance use cases by translating typologies, red flags, and control objectives into measurable model outcomes (e.g., precision/recall improvements, false-positive reduction, investigator efficiency).
• Drive & Execute research and applied innovation in supervised/unsupervised/semi‑supervised learning, graph/network analytics, anomaly detection, and weak supervision to improve true-positive rates, reduce false positives, and enhance investigator productivity.
• Develop and enhance detection modelsusing supervised/unsupervised/semi-supervised approaches (e.g., anomaly detection, clustering, weak supervision) and, where applicable,graph/network analyticsto identify complex patterns and relationships.
• Execute key parts of the model lifecycle: data sourcing (with appropriate controls), feature engineering (behavioral/temporal/entity/link features), model training, evaluation, calibration/thresholding, and performance monitoring.
• Implement interpretable ML and human-in-the-loop workflowsby supporting explainability (e.g., SHAP/LIME), stable reason codes, and feedback loops with investigators to improve usability and model precision over time.
• Contribute to MLOps and scalable deploymentby partnering with technology teams on CI/CD for ML, model registry usage, automated monitoring (data drift/concept drift), and repeatable, well-governed release processes.
• Support model risk management (MRM) deliverablesby producing documentation and analysis needed for validation (assumptions, limitations, benchmarking/challengers, back-testing, stability/drift analysis) and addressing review feedback.
• Collaborate across stakeholders(RCC, Investigations, Operations, Technology) to align on requirements, data readiness, controls, and target operating model for sustained production support.
• Apply GenAI/LLMs pragmatically(e.g., case narrative generation, unstructured text extraction/summarization) while prioritizing classical/statistical/graph ML methods where they deliver stronger, defensible detection efficacy.

Required qualifications, capabilities, and skills:

• Master’s degree(or PhD preferred) in a quantitative discipline (Computer Science, Statistics, Mathematics, Economics, Operations Research, or related).
• Minimum 4 yearsof hands‑on AI/ML experience, preferably with exposure tofinancial crime compliance / conduct risk / AML / fraud / sanctionsor similar control environments.
• Demonstrated experience building and/or deploying ML solutions (risk scoring, anomaly detection, triage/prioritization, NLP/LLM-enablement) with a focus on measurable outcomes.
• StrongPythonskills and experience with modern ML frameworks (e.g., PyTorch/TensorFlow) and common data/ML tooling.
• Practical knowledge of: imbalanced learning, cost-sensitive evaluation, feature engineering, model calibration/threshold optimization, and performance measurement in detection settings.
• Working knowledge ofMRM expectations(documentation, validation support, explainability, monitoring) in regulated financial services environments.
• Clear communication skills—able to explain model behavior, tradeoffs, and outputs (including reason codes) to technical and non‑technical stakeholders.
• Ability tomentor junior team membersthrough code reviews, pairing, and technical guidance.