Risk Management - Quant Modeling Lead - Vice President

JPMorganChase•Jersey City, NJ

About The Position

As a Risk Management Quant Modeling Lead / Vice President on the Model Risk Governance and Review Consumer & Community Banking Operations & Digital Channels team, you independently assess and challenge models and AI systems that support digital customer experiences and operational decision-making across chatbots, voice AI assistants, mobile applications, and web platforms. You collaborate with model developers, product and channel stakeholders, governance partners, and senior leadership to help ensure solutions are conceptually sound, fit for purpose, well-controlled, and compliant with the Firm’s Model Risk Management framework. You stay current with generative AI, large language models, and agentic capabilities, and translate technical findings into actionable recommendations that enhance customer outcomes and strengthen the Firm’s risk posture. The role leads independent model validation and governance across CCB Operations & Digital Channels. It evaluates traditional statistical models and advanced machine learning and AI systems, including LLM-based and agentic solutions, their performance and limitations, and their associated risk and control posture. The position communicates findings, maintains model risk controls, tracks remediation, and collaborates with stakeholders to drive timely, pragmatic risk outcomes.

Requirements

Master’s or PhD in Mathematics, Statistics, Computer Science, Engineering, Economics, Quantitative Finance, or a related field.
At least 6 years of relevant hands-on experience in model risk, model validation, quantitative risk, or applied ML oversight.
Hands-on experience with applied AI/ML and a strong understanding of GLMs, tree-based models, deep learning, transformers, LLMs, and modern AI techniques.
Strong foundation in statistics and machine learning, including estimation, testing, bias and variance, overfitting, performance measurement, and uncertainty.
Experience with Python and ML frameworks such as PyTorch, TensorFlow, XGBoost, or LightGBM.
Excellent written and verbal communication skills, including the ability to explain complex technical topics clearly and precisely.
Strong risk and control mindset, with demonstrated ability to identify, assess, and escalate model and AI risks.

Nice To Haves

Knowledge and experience with LLM technologies and modern AI patterns, including prompt engineering, RAG architectures, agentic AI systems, context engineering, agent tool use and skills, and evaluation frameworks.
Experience with conversational AI or digital assistant solutions, chat and/or voice, including practical exposure to testing, review, or ongoing performance monitoring.
Experience designing or reviewing evaluation approaches for agentic systems, such as task success or containment, human-in-the-loop review, offline test suites, and safety or quality checks.
Experience validating risk, customer experience, and/or decision support models used in digital channels or operations.
Experience working in financial services and collaborating with business, product, technology, compliance, and regulatory stakeholders.

Responsibilities

Lead and conduct independent model validation and governance activities across CCB Operations & Digital Channels.
Assess conceptual soundness, implementation accuracy, performance, limitations, and business suitability of statistical, machine learning, and AI systems used in digital journeys and operational workflows.
Review regression, decision tree, machine learning, neural network, transformer, recommender system, reinforcement learning, Generative AI, LLM-based, and agentic models and systems.
Design, review, or evaluate agentic systems, including end-to-end lifecycle considerations such as intent routing, tool use, guardrails, and evaluation and monitoring approaches.
Evaluate model and agent performance using appropriate methods, including back-testing, benchmark tests, stability and drift analysis, outcome analysis, and controlled experiments where applicable.
Assess the model risk and control posture of LLM and agentic solutions, including reliability, explainability, bias and fairness, data usage, privacy and security, prompt-injection resilience, and safe failure modes, consistent with MRGR expectations.
Communicate model risk assessments and validation findings through clear technical reports and presentations for technical and non-technical audiences.
Maintain the model risk control apparatus and serve as a primary point of contact for stakeholders across product, channel, technology, and governance.
Stay current with emerging AI and LLM developments and assess their appropriate, controlled application within business workflows.
Escalate material model issues appropriately and track remediation actions to closure.
Collaborate with model developers, business stakeholders, governance teams, and senior leadership to drive timely, pragmatic risk outcomes.