Quant Modeling Lead - Model Risk Governance Review - Vice President
hackajobJersey City (NJ)
Quant Modeling Lead - Model Risk Governance Review - Vice President
Posted 9 days ago
hackajobJersey City (NJ)
SENIORITY
Manager
About the role
Risk Management Quant Modeling Lead / Vice President 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 within 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 closely with model developers, product and channel stakeholders, governance partners, and senior leadership to ensure solutions are conceptually sound, fit for purpose, well-controlled, and compliant with the Firm's Model Risk Management framework. You stay current with emerging generative AI, large language model, and agentic capabilities, and you translate complex technical findings into clear, actionable recommendations that enhance customer outcomes and strengthen the Firm's overall risk posture.
Job 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 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
- Design, review, or evaluate agentic systems (e.g., voice bots / conversational agents) including end-to-end lifecycle considerations (intent routing, tool-use, guardrails, and evaluation/monitoring approaches)
- Evaluate model and agent performance using appropriate methodologies (e.g., back-testing, benchmark tests, stability/drift analysis, outcome analysis, and controlled experiments where applicable)
- Assess model risk and control posture for LLM/agentic solutions (e.g., reliability, explainability, bias/fairness, data usage, privacy/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, tailored to both 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/LLM developments and assess their appropriate, controlled application within business workflows
- Escalate material model issues appropriately and track remediation actions to closure
- Collaborate effectively with model developers, business stakeholders, governance teams, and senior leadership to drive timely, pragmatic risk outcomes
- 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 in model risk, model validation, quantitative risk, or applied ML oversight
- 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 (estimation, testing, bias/variance, overfitting, performance measurement, and uncertainty)
- Experience with Python and ML frameworks such as PyTorch, TensorFlow, XGBoost, or LightGBMExcellent written and verbal communication skills; ability to explain complex technical topics with clarity and precision
- Strong risk and control mindset with demonstrated ability to identify, assess, and escalate model/AI risks
- Preferred qualifications, capabilities, and skills
- Knowledge and experience with LLM technologies and modern AI patterns, including prompt engineering, RAG architectures, agentic AI systems, context engineering, agent tool-use/skills, and evaluation frameworks
- Experience with conversational AI / digital assistant solutions (chat and/or voice), including practical exposure to testing, review, or ongoing performance monitoring
- Experience in designing or reviewing evaluation approaches for agentic systems (e.g., task success/containment, human-in-the-loop review, offline test suites, and safety/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
Before you apply
Applying takes about a minute. These four things decide how fast it moves after that.
Your profile is current
It's what we read first. Occupations, seniority and locations matter more than a long history.
Two examples you can talk through
Not a portfolio — just two pieces of work where you can explain the decisions and what you'd change.
A number in mind
What you're on now and what would make you move. We negotiate better when we know both.
Your notice period
Employers plan around it, and it's the question that stalls offers most often.
Once you apply, someone reads it and calls you before anything reaches the employer — usually within two working days.