Senior Associate - Data Science / Applied AI ML

JPMorganChase; Hyderabad; On-site; INR 2,500,000 - 4,200,000; Full time

Job summary

JPMorganChase in Hyderabad seeks an experienced AI/ML specialist to deliver production-grade models for conduct risk and compliance use cases. You translate complex typologies into measurable outcomes and drive model lifecycle tasks with a strong emphasis on reliability and governance.

You will work with RCC, Investigations, and Technology to ensure data readiness, robust validation, and scalable deployment, while integrating GenAI judiciously for case narratives and extraction tasks.

Qualifications

• Master's degree (or PhD preferred) in a quantitative discipline (CS, stats, math, econ, OR).
• Minimum 4 years of hands‑on AI/ML experience, preferably in financial crime compliance / AML / fraud.
• Demonstrated experience building/deploying ML solutions with measurable outcomes.
• Strong Python skills and experience with ML frameworks (PyTorch/TensorFlow).
• Knowledge of imbalanced learning, calibration/thresholding, and performance metrics in detection.

Responsibilities

• Deliver production AI/ML solutions for CCOR conduct risk and compliance use cases.
• Drive research in supervised/unsupervised learning, graph analytics, anomaly detection, and weak supervision.
• Develop and enhance detection models with various ML approaches and graph analytics when applicable.
• Execute model lifecycle steps: data sourcing, feature engineering, training, evaluation, calibration, monitoring.
• Implement interpretable ML with explainability techniques and feedback loops with investigators.
• Contribute to MLOps and scalable deployment with CI/CD, model registry, monitoring for drift.
• Support model risk management by producing validation documentation and addressing feedback.
• Collaborate across RCC, Investigations, Operations, Technology to align requirements and data readiness.
• Apply GenAI/LLMs pragmatically while prioritizing classical ML methods for detectability.

Skills

Python

PyTorch

TensorFlow

Explainability

MLOps

Communication

Education

Master's degree in quantitative discipline

PhD preferred

Tools

CI/CD for ML

Model registry

Job description

• Deliver production AI/ML solutions for 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 models using supervised/unsupervised/semi‑supervised approaches (e.g., anomaly detection, clustering, weak supervision) and, where applicable, graph/network analytics to 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 workflows by 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 deployment by 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) deliverables by 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.

Job Description

Job Responsibilities

• Deliver production AI/ML solutions for 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 models using supervised/unsupervised/semi‑supervised approaches (e.g., anomaly detection, clustering, weak supervision) and, where applicable, graph/network analytics to 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 workflows by 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 deployment by 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) deliverables by 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 years of hands‑on AI/ML experience, preferably with exposure to financial crime compliance / conduct risk / AML / fraud / sanctions or 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.
• Strong Python skills 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 of MRM 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 to mentor junior team members through code reviews, pairing, and technical guidance.

ABOUT US

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.

About The Team

Our professionals in our Corporate Functions cover a diverse range of areas from finance and risk to human resources and marketing. Our corporate teams are an essential part of our company, ensuring that we're setting our businesses, clients, customers and employees up for success.