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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 Quant Modeling Associate Senior within the Risk Management and Compliance organization, you will play a critical role in maintaining JPMorgan Chase's strength and resilience. You help the firm grow its business in a responsible way by anticipating new and emerging risks and applying expert judgement to solve real-world challenges that impact our company, customers and communities. 

As a culture carrier in Risk Management and Compliance, you will need to encourage innovative thinking, challenge the status quo, and always strive to be best-in-class. You will be responsible for performing statistical modeling and analyses supporting the firm's fair lending program. Additionally, you will also participate in research projects to support and enhance the bank's fair lending compliance program.

JPMorgan Chase is committed to providing fair and nondiscriminatory access to credit for both individuals and communities. This commitment is central to our mission of delivering quality financial services to all customers in accordance with applicable laws. The Office of Fair Lending (OFL) oversees firm's compliance with fair lending laws through a robust Fair Lending function.

Job Responsibilities

• Develop and apply statistical models and techniques to evaluate potential fair lending disparities across home lending and related lines of business (e.g., home lending - consumer, correspondent, servicing, subservicing, commercial term lending, asset wealth management).
• Interpret and apply fair lending laws, regulations (e.g., ECOA, Fair Housing Act, Dodd-Frank Act), and related regulatory guidance (e.g., CFPB, OCC).
• Review and assess business policies, practices, and procedures to inform analytic strategies; develop quantitative and statistical models, perform independent analysis to assess fair lending risks, and make recommendations for fair lending testing.
• Prepare and maintain model documentation, evaluate model performance, and ensure compliance with Model Risk Governance and Review standards.
• Prepare presentations and reports to communicate analytical results to technical and non-technical audiences; engage with stakeholders including OFL partner teams, lines of business (LOBs), Legal, and Model Governance.
• Identify, request, and review relevant data to support ongoing and ad hoc fair lending analysis projects; identify potential gaps between data available for analysis vis-a-vis policy (e.g., underwriting attributes) and follow up with LOBs for resolution. 
• Conduct research and development to enhance analytical methods, increase process efficiency, and keep the fair lending compliance program aligned with industry and regulatory standards.
• Conduct Peer review analytical projects and provide constructive feedback.
• Perform other related duties as assigned.

Required Qualifications, Capabilities and Skills

• Graduate degree in a quantitative field (e.g., Statistics, Economics, Computer Science, Engineering). 
• Deep understanding of statistical concepts and methodologies, with the ability to apply them effectively.
• 2+ years of experience in statistics, data science, economics or a related quantitative field. 

• Proficiency in SAS, Python, SQL, and general database skills; ability to write efficient coding / macros for statistical modeling, data manipulation, and automation of analytical workflows.

• Experience with BI, GIS, or automation tools (e.g., Tableau, ArcGIS, Alteryx); demonstrated ability to leverage these platforms to analyze complex datasets, generate actionable insights, and optimize analytical processes.
• Experience developing, implementing, and evaluating statistical models (e.g., linear/logistic regression, machine learning models); demonstrated ability to select appropriate modeling techniques, validate model performance, and interpret results to support business decision-making.
• Strong critical thinking and analytical skills, with the ability to manage multiple projects in a fast-paced environment while maintaining a focus on quality.
• Willingness to learn, adapt to changes, and work collaboratively as a team player.
• Excellent verbal and written communication skills, with the ability to clearly present complex and sensitive issues to both technical and non-technical audiences.