Risk Management - Quant Modelling Lead - Vice President

JPMorgan Chase & Co.; Jersey City (NJ); On-site; USD 180,000 - 240,000; Full time

Job summary

JPMorgan Chase & Co. in New Jersey seeks a Risk Management Quant Modeling Lead/VP to independently assess and challenge marketing models that drive customer acquisition, engagement, retention, cross-sell, pricing and profitability.

You will collaborate with model developers, business stakeholders and governance teams to ensure conceptual soundness, regulatory compliance and effective communication of risk findings to senior leadership.

Qualifications

• Master's or PhD in Mathematics, Statistics, Computer Science, Engineering, Economics, Quantitative Finance, or related field
• Minimum 6 years of relevant hands-on experience
• Hands-on experience with applied AI/ML and strong understanding of GLMs, tree-based models, deep learning, transformers, LLMs, and modern AI techniques
• Strong foundation in statistics and machine learning techniques
• Experience with Python and machine learning frameworks such as PyTorch, TensorFlow, XGBoost, or LightGBM
• Excellent written and verbal communication skills
• Risk and control mindset with ability to assess and elevate model issues

Responsibilities

• Lead and conduct independent model validation and governance activities across CCB Marketing
• Assess conceptual soundness, implementation accuracy, performance, limitations, and business suitability of statistical, machine learning, and AI models
• Review traditional regression, decision tree, and advanced machine learning models, including neural networks, transformers, recommender systems, reinforcement learning, Generative AI, LLM-based solutions, and agentic systems
• Communicate model risk assessments and validation findings through technical reports and presentations
• Maintain model risk control apparatus and serve as first point of contact for stakeholders
• Stay current with emerging AI and LLM developments and assess their application within business workflows
• Provide actionable recommendations for risk management
• Collaborate with model developers, business stakeholders, governance teams, and senior leadership
• Ensure models are compliant with the Firm's Model Risk Management framework and regulatory expectations
• Escalate material model issues appropriately
• Present complex AI concepts to technical and non-technical audiences

Skills

Applied AI/ML

Python

PyTorch

TensorFlow

XGBoost

LightGBM

Communication skills

Risk mindset

Education

Master's or PhD in Mathematics, Statistics, Computer Science, Engineering, Economics, Quantitative Finance, or related field

Tools

PyTorch

TensorFlow

XGBoost

LightGBM

Job description

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 a Risk Management Quant Modeling Lead/Vice-President in the MRGR CCB Marketing team, you independently assess and challenge marketing models supporting customer acquisition, engagement, retention, cross-sell, pricing, profitability, and optimization. You work closely with model developers, business stakeholders, governance teams, and senior leadership to ensure models are conceptually sound, fit for purpose, and compliant with the Firm's Model Risk Management framework. You help us stay current with emerging AI and LLM developments and communicate actionable recommendations for risk management.

Job responsibilities:

• Lead and conduct independent model validation and governance activities across CCB Marketing
• Assess conceptual soundness, implementation accuracy, performance, limitations, and business suitability of statistical, machine learning, and AI models
• Review traditional regression, decision tree, and advanced machine learning models, including neural networks, transformers, recommender systems, reinforcement learning, Generative AI, LLM-based solutions, and agentic systems
• Communicate model risk assessments and validation findings through technical reports and presentations
• Maintain model risk control apparatus and serve as first point of contact for stakeholders
• Stay current with emerging AI and LLM developments and assess their application within business workflows
• Provide actionable recommendations for risk management
• Collaborate with model developers, business stakeholders, governance teams, and senior leadership
• Ensure models are compliant with the Firm's Model Risk Management framework and regulatory expectations
• Escalate material model issues appropriately
• Present complex AI concepts to technical and non-technical audiences

Required qualifications, capabilities, and skills:

• Master's or PhD in Mathematics, Statistics, Computer Science, Engineering, Economics, Quantitative Finance, or related field
• Minimum 6 years of relevant hands-on experience
• Hands-on experience with applied AI/ML and strong understanding of GLMs, tree-based models, deep learning, transformers, LLMs, and modern AI techniques
• Strong foundation in statistics and machine learning techniques
• Experience with Python and machine learning frameworks such as PyTorch, TensorFlow, XGBoost, or LightGBM
• Excellent written and verbal communication skills
• Risk and control mindset with ability to assess and elevate model issues

Preferred qualifications, capabilities, and skills:

• Knowledge and experience with LLM technologies, deep learning, transformers, prompt engineering, RAG architecture, agentic AI systems, context engineering, agent skills, MCP architecture, agentic harness, LLM/Agentic evaluation
• Experience validating risk, fraud, and marketing models
• Experience working in financial services and collaborating with business, technology, compliance, and regulatory stakeholders