Vice President - Data Science / Applied AI ML

JPMorgan Chase & Co.; Hyderabad; On-site; INR 900,000 - 1,500,000; Full time

Job summary

JPMorgan Chase & Co. is seeking a senior data scientist to lead production GenAI/AI/ML initiatives for risk and compliance. You will own end‑to‑end model lifecycles, build robust MLOps, and collaborate with Risk, Investigations, and Technology to meet regulatory expectations.

You will guide research in advanced ML methods, ensure model risk management, and mentor junior team members while improving detection efficacy and investigator productivity.

Qualifications

• Master’s or PhD in a quantitative discipline (CS, statistics, math, econ, OR).
• 7+ years hands-on Gen AI/AI/ML in financial crime compliance domains; regulatory knowledge.
• Proven leadership delivering production AI/ML for compliance & risk at enterprise scale.
• Advanced Python skills; strong experience with AI/ML frameworks.
• Expertise in supervised learning, anomaly detection, semi-supervised learning, clustering, feature stores.
• Model risk management: documentation, validation, benchmarking, back testing, drift analysis.
• Excellent communication to translate complex models with clear reason codes to stakeholders.
• Mentor junior team members through code reviews and pairing.

Responsibilities

• Lead the CCOR Conduct Data Science initiatives to design, deploy, and operate production-grade GenAI/AI/ML solutions across risk and compliance use cases.
• Drive research in supervised/unsupervised/semi‑supervised learning, graph analytics, anomaly detection to improve true-positive rates and reduce false positives.
• Own end‑to‑end model lifecycle: problem framing, data sourcing/controls, feature engineering, model development, validation, calibration/thresholding, bias checks, monitoring, retraining.
• Maintain rigorous model risk management across lifecycle, partnering with Model Risk and Internal Audit.
• Build and maintain robust MLOps pipelines (CI/CD for ML), registries, automated monitoring, governance artifacts.
• Partner with Risk and Compliance, Investigations, Operations, and Tech to translate regulatory expectations into defensible ML controls.
• Enhance decisioning through interpretable ML with SHAP/LIME, stable reason codes, human‑in‑the‑loop feedback; GenAI as a complement, prioritizing core ML methods.

Skills

GenAI/AI/ML

Python

Model risk management

Explainability tools

Data science leadership

MLOps CI/CD

Regulatory compliance

Stakeholder communication

Mentoring juniors

Education

Master’s or PhD in quantitative discipline

Tools

SHAP

LIME

ML frameworks

Feature stores

Job description

Job Responsibilities:

• Lead the CCOR Conduct Data Science initiatives to design, deploy, and operate production-grade GenAI/AI/ML solutions across risk and compliance use cases, with a strong focus on measurable risk mitigation and regulatory alignment.


• Drive 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.


• Own end‑to‑end model lifecycle: problem framing, data sourcing/controls, feature engineering (customer/behavioral/temporal/graph features), model development, validation, calibration/thresholding, bias/fairness checks, monitoring, and retraining.


• Maintain rigorous model risk management practices across Model lifecycle, partnering with Model Risk and Internal Audit.


• Build and maintain robust MLOps pipelines (CI/CD for ML), model registries, automated monitoring (data drift, concept drift, performance), and governance artifacts to ensure reliable, scalable production operations.


• Partner with Risk and Compliance (RCC), Investigations, Operations, and Technology to translate typologies, red flags, and regulatory expectations into defensible ML controls and measurable control effectiveness.


• Enhance decisioning through interpretable ML: deploy explainability techniques (e.g., SHAP, LIME, counterfactuals), stable reason codes, and human‑in‑the‑loop feedback loops to continuously improve model precision and usability.

  Maintain a pragmatic view of GenAI/LLMs as complementary tools (e.g., narrative generation for cases, unstructured doc parsing) while prioritizing classical/statistical/graph ML methods for core detection efficacy.

Required Qualifications and Skills:

• Master’s or PhD in a quantitative discipline (Computer Science, Statistics, Mathematics, Economics, Operations Research, or related).


• Minimum of 7 years of hands‑on Gen AI/ AI/ ML experience within Financial Crime Compliance, AML, sanctions, fraud, or related risk & compliance domains; deep knowledge of regulatory & control expectations.


• Proven leadership delivering production AI/ML for compliance & risk, including transaction monitoring models, risk scoring, anomaly detection, network/graph analytics, and/or investigator triage/prioritization at enterprise scale.


• Advanced Python skills; strong experience with AI/ML frameworks.


• Expertise in supervised learning, anomaly detection, semi‑supervised learning, clustering, feature stores, and calibration/threshold optimization; familiarity with imbalanced learning and cost‑sensitive evaluation.


• Demonstrated experience in model risk management: documentation, validation, benchmarking/challenger models, back testing, stability and drift analysis, champion/challenger governance, and explainability suitable for regulatory review.


• Excellent communication skills to translate and explain complex models with clear reason codes, and influence cross‑functional stakeholders and senior leadership.


• Ability tomentor junior team membersthrough code reviews, pairing, and technical guidance