2027 Quantitative Research - Risk and Treasury - Summer Internship - Associate - United States
J.P. Morgan·New York·United States·Risk & Compliance
J.P. Morgan is hiring a 2027 Quantitative Research - Risk and Treasury - Summer Internship - Associate - United States in New York. Posted 2026-08-03; applications close 2026-10-02 (in 57 days).
Role details
Job Summary
As a Quantitative Research – Risk and Treasury Summer Associate in the Quantitative Modeling Program, you will work alongside top-tier professionals in a dynamic environment. You will apply academic knowledge to real-world financial challenges, gain hands-on experience, and build a valuable network. The program provides a solid foundation for your career, with potential full-time offers upon successful completion.
Job Responsibilities
- Analyze data to identify patterns and trends.
- Conduct back testing and assess strategies.
- Maintain and improve software systems and tools for risk and treasury operations.
- Assess models for conceptual soundness, risks, and enhancements.
- Propose creative solutions to complex challenges.
- Collaborate with internal teams to advance risk and treasury services.
- Focus on model development and review of conceptual design.
- Develop, validate, and enhance mathematical models and algorithms.
- Optimize financial solutions across asset classes and instruments.
Required Qualifications, Capabilities, and Skills
- Enrolled in a Master’s or PhD program in mathematics, statistics, physics, engineering, computer science, economics, or data science/machine learning, graduating between December 2027 and July 2028 in the U.S.
- Proficiency in Python and/or C++.
- Strong modeling, analytical, quantitative, and problem-solving skills.
- Excellent communication skills for presenting complex concepts.
- Interest in banking analytics, global markets, and quantitative research.
- Ability to thrive in a fast-paced, collaborative environment.
Preferred Qualifications, Capabilities, and Skills
- Preferred to be enrolled in a 2-year Master’s or PhD program.
- Experience with R, MATLAB, or SQL.
- Understanding of banking products, financial instruments, and market dynamics.
- Strong organizational skills for managing multiple projects.
- Ability to articulate complex quantitative concepts to diverse audiences.
- Familiarity with AI tools used in research and programming.
Locations
- New York Metro
- Plano, TX
- Chicago, IL
Teams Available
Risk – Model Risk Governance Review Team
Team Role: The Model Risk Governance and Review (MRGR) group conducts model validation to help identify, measure, and mitigate model risk. The objective is to ensure models are used appropriately in the business context and that model users understand the models’ strengths and limitations and how these impact decisions.
Team Task: Evaluate conceptual reasonableness of assumptions, reliability of inputs and estimation methodology, completeness of testing, and correctness of implementation related to model development and use. Draft validation reports and interact with model developers.
Risk – Wholesale Credit Team
Team Role: Work as a quantitative researcher/engineer in the Wholesale Credit team, contributing to innovative credit risk solutions. Collaborate with experts to design and implement financial engineering models for wholesale credit portfolios. Apply advanced quantitative techniques to analyze credit exposures and forecast performance under various scenarios. Support the development of new methodologies to address emerging risks in wholesale lending.
Team Task: Develop statistical, AI/ML, and platform engineering solutions for grading, forecasting, and risk management activities. Build robust models to assess borrower creditworthiness and predict potential defaults. Enhance risk management frameworks by integrating data-driven insights and automation. Partner with business stakeholders to deliver actionable analytics that inform strategic credit decisions.
Treasury – Quantitative Balance Sheet Strategy Team
Team Role: Quantitative Balance Sheet Strategy (QBSS) delivers quantitative analytics to support JPMorganChase’s Global Corporate Treasury in Asset & Liability Management and balance sheet strategy. Key functions include ALM, research, capital and liquidity strategy, quantitative modeling and analytics across all lines of business. QBSS supports initiatives such as firm-wide balance sheet optimization, interest rate risk modeling, internal transfer pricing, capital allocation, and research on financial flows.
Team Task: Provide interns broad exposure to business activities under the Corporate Treasury umbrella. A typical summer internship includes one or two major projects and multiple ad-hoc exercises. Past projects have included interest rate risk forecasting, liquidity position modeling, capital buyback optimization, risk-adjusted performance measurement for investment portfolio allocation, balance sheet optimization via dimensionality reduction, analysis of industry-wide deposit flow dynamics, and market share evolution in major lending markets.
About You
We are looking for innovative problem-solvers with a passion for developing complex solutions that support our global business.
About Us
At JPMorganChase, we create positive change for the diverse communities we serve through a supportive culture that helps you every step of the way as you build your career.
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Applying to this role
This 2027 Quantitative Research - Risk and Treasury - Summer Internship - Associate - United States role at J.P. Morgan runs through the firm's own careers portal and expects a CV and cover letter written specifically for the posting, not a portable submission carried across firms. Jorb AI's application agent tailors a CV and cover letter from your background to this posting and tracks the role alongside the rest of your applications.
Jorb AI tracks details for 2027 Quantitative Research - Risk and Treasury - Summer Internship - Associate - United States at J.P. Morgan. Postings refresh hourly from primary careers pages. Job details mirror the firm's posting; the apply link goes directly to the source. Last refreshed 2026-08-06.
