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 Model Validation Analyst in the Risk Management and Compliance team, you help us shape business strategy and drive innovation. You use your expertise to protect the firm through rigorous model validation and risk management. You collaborate with diverse teams to solve real-world challenges and support our company, customers, and communities. You play a key role in keeping JPMorgan Chase strong and resilient.
Job responsibilities:
- Conduct independent model validation and governance activities to assess model soundness, mitigate model risk with a focus on AI/ML models (e.g. XGB, Neural Networks, Reinforcement Learning, Recommender Systems, as well as variations of Regression algorithms), LLM-based frameworks, Generative AI, and agentic systems.
- Remain current with emerging AI and LLM developments, get hands-on with new capabilities to understand their strengths and limitations, assess how they can be applied within business workflows, and communicate actionable recommendations for risk management to stakeholders.
- Validate models to ensure accuracy and reliability
- Assess and manage risks across business functions
- Collaborate with cross-functional teams to drive innovation
- Develop and implement model validation frameworks
- Communicate findings and recommendations to stakeholders
- Monitor emerging risks and regulatory changes
- Support business growth through responsible risk management
- Document validation processes and results
- Provide expert judgment on model performance
Required qualifications, capabilities, and skills
- Master's or PhD degree in a quantitative discipline such as Mathematics, Statistics, Computer Science, Engineering, Economics, Finance, or a related field, with strong quantitative and analytical skills.
- Hands-on experience with applied AI/ML. Knowledge and experience with the following preferred: LLM technologies, including deep learning, transformers, prompt engineering, RAG architectures, agentic AI systems, context engineering, agent skills, MCP architecture, agentic harness, LLM evaluation and beyond.
- Strong foundation in statistics, econometrics, and machine learning techniques, with a deep understanding of model assumptions, limitations, explainability, and performance evaluation.
- Strong communication skills with the ability to present complex AI concepts to both technical and non-technical audiences. A risk and control mindset with the ability to ask incisive questions, assess the materiality of model issues, and escalate appropriately
- Strong analytical and problem-solving skills
- Attention to detail and commitment to quality
- Ability to work independently and as part of a team
- Experience with data analysis tools (e.g., Python, R)
- Understanding of risk management principles
- Professional judgment and integrity
Preferred qualifications, capabilities, and skills
- Experience in financial services or banking
- Experience in model validation or risk management
- Familiarity with machine learning models
- Advanced proficiency in programming languages
- Experience with model governance frameworks
- Knowledge of emerging risk trends
- Leadership or mentoring experience
