Quantitative Counterparty Credit Risk Associate

JPMorgan Chase & Co.; Jersey City (NJ); Sur place; USD 120 000 - 170 000; Plein temps

Résumé du poste

JPMorgan Chase & Co. in Jersey City seeks a Quantitative Research Associate to develop and maintain risk models used to manage counterparty risk across the derivatives portfolio.

You will work with risk, technology, and research partners globally to advance stressed exposure methodologies and ensure robust risk calculations. The role emphasizes applying deep quantitative skills to real-world financial challenges, contributing to a world-class risk framework while growing within a collaborative,

Qualifications

• Formal training in data science concepts and 2+ years of applied experience.
• Advanced degree (PhD, MSc, or equivalent) in Engineering, Mathematics, Physics, Computer Science, or a related quantitative discipline.
• Proficiency in Python, with the ability to write clean, efficient, and well-documented code.
• Demonstrated experience in quantitative finance or applied mathematics, with the ability to translate theoretical concepts into practical solutions.
• Strong analytical and problem-solving skills, with a track record of working through complex, ambiguous challenges.
• Excellent communication and collaboration skills, with the ability to work effectively across technical and non-technical teams in a global environment.

Responsabilités

• Design and implement enhancements to the counterparty credit risk framework, ensuring models remain robust, accurate, and aligned with evolving regulatory and business requirements.
• Conduct quantitative analysis leveraging the firm's infrastructure to evaluate model performance and support methodological development.
• Collaborate with risk and technology partners to jointly manage the full model lifecycle, from development through validation and production deployment.
• Provide timely and accurate support for business requests, translating complex quantitative concepts into actionable insights for stakeholders.
• Monitor ongoing performance of the calculation framework and contribute to governance processes that ensure model integrity and compliance.
• Partner closely with Quantitative Research teams across global locations to share knowledge, align methodologies, and drive consistency.
• Produce clear and thorough documentation of modeling choices, theoretical frameworks, testing procedures, and results to support transparency and auditability.

Connaissances

Python

Quantitative finance

Analytical skills

Collaboration

Data science concepts

Formation

PhD or MSc in quantitative discipline

Outils

C++

Description du poste

If you are passionate about quantitative finance, thrive in a collaborative environment, and want to make a measurable impact on how a global financial institution manages risk, this is your opportunity. Join a team at the forefront of counterparty credit risk modeling, where your work directly shapes the firm's ability to manage complex derivatives exposures at scale.

As a Quantitative Research Associate at JPMorgan Chase within the Quantitative Research team, you will contribute to the development and maintenance of models that produce critical risk metrics used to manage counterparty risk across the firm's derivatives portfolio. Based in Jersey City, you will collaborate with risk, technology, and quantitative research partners across the globe to advance stressed exposure methodologies and ensure the integrity of the firm's risk calculation framework. This role offers a unique opportunity to apply deep quantitative skills to real-world financial challenges while growing within a world-class team.

Job responsibilities

• Design and implement enhancements to the counterparty credit risk framework, ensuring models remain robust, accurate, and aligned with evolving regulatory and business requirements
• Conduct quantitative analysis leveraging the firm's infrastructure to evaluate model performance and support methodological development
• Collaborate with risk and technology partners to jointly manage the full model lifecycle, from development through validation and production deployment
• Provide timely and accurate support for business requests, translating complex quantitative concepts into actionable insights for stakeholders
• Monitor ongoing performance of the calculation framework and contribute to governance processes that ensure model integrity and compliance
• Partner closely with Quantitative Research teams across global locations to share knowledge, align methodologies, and drive consistency
• Produce clear and thorough documentation of modeling choices, theoretical frameworks, testing procedures, and results to support transparency and auditability

Required qualifications, capabilities, and skills

• Formal training or certification on data science concepts and 2+ years applied experience
• Advanced degree (PhD, MSc, or equivalent) in Engineering, Mathematics, Physics, Computer Science, or a related quantitative discipline
• Proficiency in Python, with the ability to write clean, efficient, and well-documented code
• Demonstrated experience in quantitative finance or applied mathematics, with the ability to translate theoretical concepts into practical solutions
• Strong analytical and problem-solving skills, with a track record of working through complex, ambiguous challenges
• Excellent communication and collaboration skills, with the ability to work effectively across technical and non-technical teams in a global environment

Preferred qualifications, capabilities, and skills

• Experience in counterparty credit risk, including familiarity with exposure methodologies such as stressed exposure or potential future exposure
• Proficiency in C++, particularly in the context of quantitative or financial modeling applications
• Working knowledge of derivatives across multiple asset classes, including rates, credit, equities, or commodities
• Exposure to model governance, validation frameworks, or regulatory requirements related to counterparty risk
• Experience working in a cross-functional or globally distributed team environment