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About JPMorgan Chase
JPMorgan Chase & Co. is a global financial services firm and one of the largest banks in the world, offering investment banking, commercial banking, asset management, and consumer financial services.
JPMorgan Chase currently has
1117 open roles
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Listed pay typically runs
$186,160–$215,000
across 7 roles with salary data.
As a Risk Management - Model Risk Program Associate within the Risk Management and Compliance team, you will perform independent model validation and governance activities to ensure accuracy and reliability. You will focus on mitigating risks associated with AI/ML models, including XGB, Neural Networks, Reinforcement Learning, Recommender Systems, and Regression algorithms. Your daily work involves evaluating LLM-based frameworks, Generative AI, and agentic systems while staying current on emerging technologies like transformers, prompt engineering, RAG architectures, and MCP architecture. You will develop validation frameworks, document results, and communicate actionable recommendations to stakeholders. To succeed, you must possess a master's or PhD in a quantitative discipline and proficiency in Python or R. The role requires deep knowledge of statistics, econometrics, and machine learning techniques to address complex model performance issues within the financial services domain.
Conduct independent model validation and governance to assess soundness and mitigate risks for AI/ML, LLM, and Generative AI systems.
Evaluate the accuracy and reliability of models used across various business functions.
Stay current on emerging AI developments to provide actionable risk management recommendations to stakeholders.
Develop and implement comprehensive model validation frameworks.
Monitor emerging risks and regulatory changes to support responsible business growth.
Document all validation processes, results, and expert judgments regarding model performance.
Communicate complex technical findings and risk assessments to both technical and non-technical audiences.
What we're looking for
Master's or PhD degree in a quantitative discipline such as Mathematics, Statistics, Computer Science, Engineering, Economics, Finance, or a related field.
Hands-on experience with applied AI/ML.
Knowledge and experience with LLM technologies, including deep learning, transformers, prompt engineering, RAG architectures, agentic AI systems, context engineering, agent skills, MCP architecture, agentic harness, and LLM evaluation (preferred).
Strong foundation in statistics, econometrics, and machine learning techniques with an understanding of model assumptions, limitations, explainability, and performance.
Experience with data analysis tools such as Python or R.
Strong communication skills to present complex AI concepts to both technical and non-technical audiences.
Experience in financial services or banking (preferred).
Experience in model validation, risk management, or model governance frameworks (preferred).
Generative AI
Machine Learning
Python
R
SAS
SQL
Vertex AI
LangChain
LangGraph
HuggingFace
OpenAI APIs
RAG
Prompt Engineering
Neural Networks
Model Risk Management
Actuarial Science
Data Science