Risk Management - CCB Marketing Model Review Lead - Vice President

JPMorgan Chase & Co.•Jersey City, NJ

1d•$147,250 - $215,000

About The Position

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.

Requirements

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 escalate model issues

Nice To Haves

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

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