Bring your expertise to JPMorganChase. As part of Risk Management and Compliance, you are at the center of keeping JPMorganChase 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 Credit Capital Model Development Vice President within the Wholesale Credit Risk Management Quantitative Research team, you will utilize your experience in modeling of either balance forecasting (commitment, utilization, prepayment) and/or spread/GII/NII and focus on Pre-Provision Net Revenue (PPNR) stress modeling methodologies.
Job Responsibilities
Develop statistical models and frameworks for PPNR forecasting under different economic scenarios. Work across the entire team , including planning, analysis, development, and testing of new applications and enhancements to existing applications. Ensure adherence to best practices and standards throughout the development process. Perform data analysis to support model development and analytics and collaborate with LOBs and LOB finance teams to ensure integrity and accuracy in model results Liaise with various lines of business to thoroughly understand various models for CCAR, CECL and other credit risk applications. Ensure alignment with regulatory requirements and internal governance standards. Define data models, metadata, and data dictionary that will enable data analysis and analytical explorations. Implement data governance practices to ensure data quality, consistency, and compliance with organizational policies. Participate in stress testing exercises: CCAR, RA, IFRS9Required Qualifications, Capabilities and Skills
Masters degree in finance, statistics, econometrics, mathematics, physics, engineering or similar quantitative discipline Minimum 3 years of experience Solid theoretical and practical knowledge of statistical methods and models: generalized linear models, time-series analysis, clustering, decision trees logistic regression Basic knowledge on credit risk modeling both at single-obligor level and portfolio level Experience in handling large amount of panel data and data cleaning/filtering Hands on programming in Python Previous experience in writing documents for regulatory reviewsPreferred Qualifications, Capabilities and Skills
Prior experience in wholesale credit Prior experience in PPNR modeling Hands on programming experience in R