JP Morgan Chase - Associate - Wholesale Credit Portfolio Analytics

JPMorgan Chase is a leading global financial services firm with assets of $2.5+ trillion and operations in more than 60 countries. The firm is a leader in investment banking, commercial banking, financial services for small business and consumers, financial transaction processing, asset management and private equity.

The Wholesale Ratings team is responsible for implementing key credit risk practices across Wholesale businesses and ensuring consistency in methodologies within Wholesale Credit Risk. The team's areas of responsibility include risk grading methodology and the provision of data-driven insights to the originating businesses.

Job Summary and Responsibilities:

- Primary responsibility of this role is to deliver data driven insights into the credit risk organization using business intelligence tools and big data infrastructure

- Process both internal and external datasets using SQL and Python for analytics use cases

- Utilize visualization tools such as Tableau, and prepare presentation materials for various stakeholders including senior management

- Apply new and emerging analytical methods on real world data for the purposes of building solutions with a direct impact on the business line

- Engage with stakeholders within credit organization to identify where data can inform / automate decision making

- Assist with periodic updates to grading template updates and other reporting owned by the team

Qualifications:

- Degree in a computer science, mathematics or finance program

- Experience and knowledge to write SQL and Python code for data analysis on large datasets is prerequisite - scripting, numpy, scipy, matplotlib, scikit-learn, jupyter notebooks is recommended

- Experience in data analysis and dealing with large quantities of data

- Prior experience with Excel and VBA

- Experience in visualization tools like Tableau

- Experience with machine learning algorithms, such as neural networks/deep learning, SVM, Random Forest, linear regression, etc.

- Detail oriented and strong organizational skills

- Excellent communication abilities, both written and oral; comfortable with presenting information to key senior stakeholders

- Ability to solve problems creatively while working in a dynamic and challenging environment under tight deadlines

- Eagerness to learn about credit risk, risk parameters, stress testing and loss forecasting

- Additional qualifications/experience considerations: CFA/FRM a plus