Vice President - Data Science / Applied AI ML

Next Frontier Capital; Hyderabad; On-site; INR 24,015,000 - 43,228,000; Full time

Job summary

JPMorganChase is seeking a Vice President of Data Science / Applied AI & ML to lead production AI/ML initiatives across risk and compliance. You will drive research, own the end-to-end model lifecycle, and ensure robust governance and explainability for regulatory reviews.

The role requires 7+ years of GenAI/ML experience in financial crime contexts, advanced Python skills, and proven leadership delivering enterprise-scale solutions.

Qualifications

• 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 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.
• Demonstrated experience in model risk management: documentation, validation, benchmarking/challenger models, back testing, stability and drift analysis, 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 to mentor junior team members through code reviews, pairing, and technical guidance

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, 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 while prioritizing classical/statistical/graph ML methods for core detection efficacy.

Skills

Python

AI/ML frameworks

GenAI/AI/ML

Leadership

Regulatory compliance knowledge

Model risk management

Communication

Mentoring

MLOps / Cloud

Education

Master’s or PhD in quantitative discipline

Tools

TensorFlow

PyTorch

Scikit-learn

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 to mentor junior team members through code reviews, pairing, and technical guidance

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.

Vice President - Data Science / Applied AI ML