CCB Risk Program Associate - Quantitative Modeler
CCB Risk Program Associate – Quantitative Modeler
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, will be a key member of the Portfolio Risk Modeling team. The successful candidate will be responsible for end-to-end model design and development. The candidate will build a solid understanding of various consumer products and key risk drivers and use that to further enhance the models. Team will answers complex and unique questions, utilizing cutting edge quantitative methods and leverage one of the world’s largest repositories of consumer lending data. By applying expert knowledge in predictive modeling, along with a deep understanding of the businesses of consumer lending, we partner with teams across JPMC to assess, measure, and manage critical risks within the CCB consumer loan portfolios.
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.
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