CCB Risk Program Associate
at J.P. Morgan
Posted 15 days ago
No clicks
- Compensation
- Not specified
- City
- Wilmington
- Country
- United States
Currency: Not specified
Join the Portfolio Risk Modeling team to develop and maintain forecasting models for Chase credit card portfolios used in stress testing, loss reserves, and business planning. You will perform feature engineering and train machine learning models on very large transaction datasets, working with a large distributed codebase. The role requires collaboration with business partners across loss forecasting, finance and technology and clear communication of analytical findings to stakeholders and senior leadership. Strong programming skills (Python or Scala) and a quantitative master's degree are required; big-data tooling (Spark/Hive) and a PhD are preferred.
Location: Wilmington, DE, United States
The Portfolio Risk Modeling team within CCB Risk Modeling group is responsible for end-to-end development of best in class forecasting model suite for Chase credit card portfolios to support stress testing, loss reserve, and business planning exercises.
Job Responsibilities:
- You will work on a large and cleanly structured codebase designed for large-scale distributed simulation and forecasting.
- collaborate with business partners in loss forecasting, finance and technology, effectively communicate model results, analytical findings, and insights to them and senior leadership team to support business and or technical decisions.
- Perform machine learning tasks such as feature engineering, feature selection, and developing and training machine learning algorithms using cutting-edge technology to extract predictive models/patterns from billions of transactions’ amounts of data.
- Collaborate with business teams to identify opportunities, collect business needs, and provide guidance on leveraging the machine learning solutions.
- Interact with a broader audience in the firm to share knowledge, disseminate findings, and provide domain expertise
Job requirements, capabilities and skills:
- Master’s degree in Computer Science, Mathematics, Statistics, Econometrics, Physics, Engineering, or a related quantitative discipline is required.
- Proven proficiency in programming languages for large-scale data analysis, such as Python or Scala.
- A strong interest in how models work, the reasons why particular models work or not work on particular problems, and the practical aspects of how new models are designed.
Preferred qualifications, capabilities and skills:
- PhD in a quantitative field with publications in top journals, preferably in machine learning.
- Experience with model design in a big data environment making use of distributed/parallel processing via Hadoop, particularly Spark and Hive
