Risk Management - Quant Modeling Director - Executive Director
JPMorgan Chase & Co., New York, NY, United States
Bring your Expertise to JPMorgan Chase. As part of Risk Management and Compliance, you are at the center of keeping JPMorgan Chase strong and resilient. You help the firm grow its business in a responsible way by anticipating new and emerging risks, and using your expert judgement to solve real-world challenges that impact our company, customers and communities. Our culture in Risk Management and Compliance is all about thinking outside the box, challenging the status quo and striving to be best-in-class.
As an Executive Director in the Model Risk Governance and Review team for Consumer and Community Banking, you will lead the validation and risk governance of marketing machine learning models used across the business. You will evaluate conceptual soundness, assess assumptions and input reliability, confirm testing completeness and numerical robustness, and ensure the suitability of performance metrics while designing experiments to quantify limitations and benchmark results. You will maintain model risk controls, elevate and resolve issues promptly, and keep current with advanced artificial intelligence and machine learning techniques, risk management practices, and industry standards as you help train and develop teammates. You will collaborate with model developers and users across Risk, Finance, Operations, Digital, Marketing, Fair Lending, Technology, Control teams, Internal Audit, and bank regulators to ensure models are used appropriately and transparently in their business context.
Job Responsibilities
Lead and develop a team within MRGR for CCB to validate and govern Marketing models used across CCB.
Lead validation of ML‑based Marketing models within CCB; demonstrate deep ML proficiency, recognize strengths and limitations, apply regulatory expectations to ML, and articulate the appropriateness of model techniques.
Apply and master standard ML tools and programming languages to support robust model assessment.
Execute comprehensive model reviews by evaluating conceptual soundness, assessing assumption reasonableness and input reliability, verifying implementation testing completeness, confirming numerical robustness, and justifying performance metrics and risk measures.
Design and implement experiments to quantify model risk, including impacts from model limitations, parameter estimation error, and deviations from assumptions.
Benchmark model outputs against empirical evidence and alternative/benchmark models to validate performance and stability.
Collaborate with stakeholders across Model Development and Users (Risk, Finance, Operations, Digital, Marketing), MRGR, Fair Lending, Technology, Control functions, Internal Audit, and regulators to drive transparent outcomes.
Maintain model risk controls for CCB models, serve as the first point of contact, and identify, elevate, and resolve issues promptly and soundly.
Track and synthesize advances in modeling techniques (advanced AI/ML), products, markets, risk management practices, and industry standards relevant to CCB.
Mentor and train junior team members to accelerate their learning, capability building, and professional development.
Required qualifications, skills and capabilities
PhD or Master Degree in Statistics, Economics (with a focus on Econometrics), Data Science, Computer Science, Operations Research, Physics, Engineering, Applied Math or a quantitative discipline.
10+ years of relevant experience, with at least 5+ years of experiencein applied quantitative research or model development for retail financial products, including machine learning experience, with demonstrated leadership excellence.
In depth knowledge ofmachine learningtechniques (supervised and unsupervised), natural language processing, data mining as well as experience in probability theory, econometrics, statistics, numerical methods.
Product domain expertise in retail (consumer) banking products and ability to understand the business / knowledge of regulation surrounding business
Excellent analytical and problem solving abilities.
Risk & Control mindset: Inquisitive nature, ability to ask right questions and elevate issues.
Excellent communication skills (written and verbal).
Experience managing teams.
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As an Executive Director in the Model Risk Governance and Review team for Consumer and Community Banking, you will lead the validation and risk governance of marketing machine learning models used across the business. You will evaluate conceptual soundness, assess assumptions and input reliability, confirm testing completeness and numerical robustness, and ensure the suitability of performance metrics while designing experiments to quantify limitations and benchmark results. You will maintain model risk controls, elevate and resolve issues promptly, and keep current with advanced artificial intelligence and machine learning techniques, risk management practices, and industry standards as you help train and develop teammates. You will collaborate with model developers and users across Risk, Finance, Operations, Digital, Marketing, Fair Lending, Technology, Control teams, Internal Audit, and bank regulators to ensure models are used appropriately and transparently in their business context.
Job Responsibilities
Lead and develop a team within MRGR for CCB to validate and govern Marketing models used across CCB.
Lead validation of ML‑based Marketing models within CCB; demonstrate deep ML proficiency, recognize strengths and limitations, apply regulatory expectations to ML, and articulate the appropriateness of model techniques.
Apply and master standard ML tools and programming languages to support robust model assessment.
Execute comprehensive model reviews by evaluating conceptual soundness, assessing assumption reasonableness and input reliability, verifying implementation testing completeness, confirming numerical robustness, and justifying performance metrics and risk measures.
Design and implement experiments to quantify model risk, including impacts from model limitations, parameter estimation error, and deviations from assumptions.
Benchmark model outputs against empirical evidence and alternative/benchmark models to validate performance and stability.
Collaborate with stakeholders across Model Development and Users (Risk, Finance, Operations, Digital, Marketing), MRGR, Fair Lending, Technology, Control functions, Internal Audit, and regulators to drive transparent outcomes.
Maintain model risk controls for CCB models, serve as the first point of contact, and identify, elevate, and resolve issues promptly and soundly.
Track and synthesize advances in modeling techniques (advanced AI/ML), products, markets, risk management practices, and industry standards relevant to CCB.
Mentor and train junior team members to accelerate their learning, capability building, and professional development.
Required qualifications, skills and capabilities
PhD or Master Degree in Statistics, Economics (with a focus on Econometrics), Data Science, Computer Science, Operations Research, Physics, Engineering, Applied Math or a quantitative discipline.
10+ years of relevant experience, with at least 5+ years of experiencein applied quantitative research or model development for retail financial products, including machine learning experience, with demonstrated leadership excellence.
In depth knowledge ofmachine learningtechniques (supervised and unsupervised), natural language processing, data mining as well as experience in probability theory, econometrics, statistics, numerical methods.
Product domain expertise in retail (consumer) banking products and ability to understand the business / knowledge of regulation surrounding business
Excellent analytical and problem solving abilities.
Risk & Control mindset: Inquisitive nature, ability to ask right questions and elevate issues.
Excellent communication skills (written and verbal).
Experience managing teams.
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