The Model Risk Internal Audit group is a global team of quantitative specialists who assess highly technical model risks and the effectiveness of controls for complex models used across a diverse product portfolio and all lines of business. You will engage with both senior PhD quants and non-technical senior leaders—challenging, influencing, and improving how model risk is managed throughout the model lifecycle.
As a Quant Model Risk Auditor within Internal Audit Model Risk Management Team, you will play a central role in helping keep JPMorganChase strong and resilient. You will support responsible, controlled business growth by anticipating emerging risks and applying expert judgment to solve real-world challenges that affect our company, customers, and communities. Our Internal Audit culture emphasizes fresh thinking, constructive challenge, and a relentless focus on being best-in-class.
You will also develop and implement advanced AI/ML solutions to perform model risk control assessments that inform strategic decisions about the firm’s model risk profile and management framework, while improving the efficiency and rigor of control testing.
Joining the Model Risk team places you at the heart of the firm’s model risk management framework, with exposure to a broad range of model types and leading-edge techniques, and frequent interaction with top talent across the firm. You will deepen your understanding of a wide variety of models used throughout the organization, their unique limitations, and how to apply that knowledge to shape business strategy, strengthen the control environment, and protect the firm.
Job responsibilities:
- Perform highly technical reviews of complex models across all lines of business and corporate functions to evaluate model risk and determine whether it is appropriately mitigated by effective controls. Reviews broadly include assessing conceptual soundness, model design, appropriateness of use, implementation and performance testing results, overall fitness for purpose and determining if the model(s) was developed in accordance with internal policies and applicable external standards.
- Evaluate ongoing model performance programs to ensure the defined metrics and thresholds are suitable for identifying performance problems and monitoring the model remains fit for purpose throughout its usage life cycle.
- Conduct sophisticated in-depth analysis and control assessments of a highly technical, complex global model risk management framework across all stages of the model lifecycle (usage, development, validation, governance, ongoing management) to confirm that defined controls effectively mitigate model risk and provide recommendations to remediate identified control gaps.
- Effectively partner (challenge, influence) directly with quantitative professionals (PhDs) and senior management stakeholders and communicate identified issues and influence the allocation of resources to address identified model risk control gaps.
- Develop end-to-end (design, architecture, implementation, user experience ) highly complex AI/ML product solutions/models, using advanced data analytics for targeted testing and model risk control assessments to inform strategic decisions on the firm’s model risk profile and associated management framework, while enhancing the efficiency and rigor of model risk control testing methodologies.
- Lead and manage end-to-end model risk control audit assessments (scoping, planning fieldwork) through execution.
- Perform validation of business-implemented model risk remediation actions addressing issues identified by regulators
Required Qualifications, Capabilities and Skills:
- Strong quantitative and analytical skills: PhD or Masters S.T.E.M. Degree in Mathematics, Data Science, Financial Engineering, Quantitative Finance, Computer Science, AI/ML or a related field.
- 2+ years of experience in one or more of the following business roles; AI/ML product development, quantitative model development (including AI/ML), model validation, model development, data science or related fields
- Proficient to expert in programming/engineering languages (e.g., Python, R) and AI/ML development techniques (e.g., LangChain/LangGraph, SDKs) to design, develop, and implement models, with a strong understanding of the code and architecture underlying AI/ML solutions (e.g., GenAI, agentic systems, LLMs).
- Very strong communication skills (both verbal and written) for writing highly technical reports and driving and influencing change on model-related issues
- Excellent risk-and-control mindset, applying strong critical thinking and analytical/quantitative skills to quickly synthesize and document model risk control insights, identify critical issues, ask thoughtful questions, and escalate complex, sensitive matters to both technical and non-technical stakeholders.
- Very strong organizational and leadership skills to lead end-to-end business model risk control assessments and strategic projects.