At JPMorgan, we are not looking for job seekers. We seek change makers who want to make an impact.

As a Risk Program Senior Associate within our dynamic team, you will play a pivotal role in the Portfolio Risk Modeling team. Your responsibilities will encompass the complete cycle of model design and development, fostering a robust understanding of diverse consumer products and key risk factors to further refine the models. Our team addresses intricate and unique queries, employing state-of-the-art quantitative methods and capitalizing on one of the world's largest databases of consumer lending data. You will be part of a team of quantitative professionals developing and maintaining advanced credit risk forecasting models for the evaluation of Consumer and Community Banking's retail portfolios, which are utilized for both regulatory and portfolio risk management purposes. This opportunity is specific to the Auto and Business Banking Forecasting Model Development Team within Portfolio Risk Modeling.

 Job Responsibilities -

Design, develop, test, and validate statistical/economic models for consumer/retail portfolios, including probability of default, loss given default, and exposure at default.    Utilize state-of-the-art modeling including both classical statistical modeling approaches and modern machine learning approaches to enhance existing models and tackle challenging modeling problems Manage end-to-end model development process, including data manipulation, exploratory data analysis and pattern discovery, model development, refinement and validation, documentation, assisting with implementation, and performance monitoring Collaborate with cross functional partners in Risk, Finance, Technology, Model Governance throughout the entire modeling life cycle.

Required qualifications, capabilities and skills -

Advanced degree in a quantitative discipline (e.g. Mathematics, Statistics, Economics, Computer Science, Operations Research) - Masters with 2+ years of relevant working experience or a PhD. Strong data analysis and statistical/economic modeling experience, such as generalized linear models, multivariate analysis and time series analysis Proficiency in advanced analytical languages (e.g. SAS, Python, R);  Ability to work with large data and perform extensive analysis to draw useful insights Strong communication skills to present to and collaborate with business partners and model end-users Strong organizational and multi-tasking skills with demonstrated ability to manage expectations and deliver quality results on time Comfortable working both independently and in a team environment.

Preferred qualifications, capabilities and skills 

Credit risk modeling experience is a plus, but not necessary. Familiarity with framework of machine learning pipeline (e.g. tensor flow, scikit-learn) is not required but a plus.