Senior Internal Audit Associate - Model Risk

Join JPMorgan Chase’s industry leading Model Risk Artificial Intelligence/Machine Learning team within Internal Audit, which conducts independent strategic model risk control assessments and provides outcomes to senior executives across the globe on the firm’s model risk management framework.

As an Internal Audit Associate within the Model Risk Artificial Intelligence/Machine Learning Audit team, you will have exposure to a variety of modeling techniques (AI/ML and non-AI/ML) and their unique limitations spanning all lines of business (associated products, services and quantitative risk management practices) and use the knowledge to shape business strategy and protect the firm. You will be responsible for designing and developing cutting-edge AI/ML solutions to enhance the efficiency and effectiveness as we execute our model risk coverage strategy. In addition, you will be responsible for conducting independent model risk control assessments to help us anticipate, navigate and assess emerging risks and evaluate controls intended to mitigate model risk across the end-to-end model lifecycle

The team’s mission is to independently support the firm and to shape the business strategy by ensuring a robust model risk management framework is in place for the firm, our customers and the communities we serve around the world. Our mission is supported by a culture focused on team work, thinking outside the box and challenging the status quo and our position as a best-in-class, industry leading Model Risk Artificial Intelligence/Machine Learning Internal Audit team.

Job Responsibilities

• Design, develop and implement AI/ML solutions to automate, optimize and enhance model risk analysis and control testing
• Evaluate adherence to model development standards, processes, policy and procedures through conducting robust data driven quantitative and qualitative analysis and directly partnering with model developers, model users, model reviewers and governance oversight personnel.
• Document in high quality well written technical reports model risk control assessments and communicate findings to stakeholders including the Chief Auditor.
• Identify control weaknesses and/or gaps in the firm’s model risk management framework
• Set standards for robust model risk control assessments and enhance them as needed to adapt to changes in the firm’s model risk management framework and/or meet evolving regulatory standards.
• Lead, plan, manage and execute end-to-end line of business model risk analysis and control assessments and strategic team projects
• Validate strategic solutions implemented by the firm to address regulatory identified issues related to the firm’s model risk management framework and practices

Required qualifications, capabilities and skills

• PhD or Masters Degree in Mathematics, Data Science, Financial Engineering, Computer Science, Artificial Intelligence Machine Learning or a related field.
• 2 plus years of experience in one or more of the following business roles; AI/ML product development, quantitative model development (including machine learning), model validation, data science or related fields
• Strong leadership and communication skills (both verbal and written) for writing highly technical reports and driving and influencing change on model-related issues
• Understanding of and proficiency in programming languages (e.g. Python, R, or equivalent)
• Understanding of and proficiency in developing and using AI/ML solutions (e,g, Gen-AI, agentic AI, traditional AI) and frameworks (e.g. pytest) to ensure code quality and reliability
• Understanding of and proficiency in statistics, derivative pricing and machine learning modeling techniques
• Risk and control mindset with the ability to ask incisive questions, converge on critical matters, assess materiality, and escalate complex and sensitive issues to senior management
• Strong critical thinking and analytical/quantitative skills to perform model risk analysis against the controls in place intended to mitigate the associated risk.