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.

Job summary

As a Wholesale Credit Risk Associate in the Wholesale Credit Quantitative Research Model Forecast Implementation Team, you design and implement modern risk modeling platforms and algorithms that power wholesale credit decisions. You partner with model developers and business stakeholders to translate complex analytics into scalable solutions. You grow your skills across quantitative modeling, numerical methods, and software engineering while contributing to resilient risk management.

Job responsibilities

• Develop statistical and quantitative risk models for wholesale credit portfolios.
• Design, implement, and maintain JPMorgan Chase wholesale credit models, including stress testing and credit reserve requirements; support model backtesting and validation.
• Implement efficient numerical algorithms using Python and optimized C libraries.
• Build object-oriented software for risk analytics and integrate new models into the Firmwide Forecasting Framework.
• Analyze and debug unexpected forecast behaviors to improve accuracy and robustness.
• Conduct peer code reviews to elevate quality and performance of the forecasting framework.
• Collaborate with model developers and business partners to implement, test, and operationalize forecasting capabilities.
• Present progress, findings, and roadmap updates to senior leaders and modeling teams.
• Manage project deliverables, defect remediation, and new feature releases across model development cycles.
• Review, implement, and test technical documentation to ensure clarity and compliance.

Required qualifications, capabilities, and skills

• Master’s degree or equivalent education in computer science, data science, mathematics, statistics, financial engineering, or related fields.
• Proficiency in object-oriented programming using C++ and Python.
• Strong knowledge of tools and methods for exploratory data analysis, such as Pandas and Num Py, which leverage efficient low-level C implementations.
• Experience with statistical modeling and Monte Carlo simulation.
• Ability to work with large datasets.
• Experience designing and consuming RESTful APIs for quantitative workflows (credit data, risk calculators, model executions).

Preferred qualifications, capabilities, and skills

• Experience with LLMs, prompt engineering, and AI-based agent coding tools is a plus.