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 Credit Analytics and Solutions team (WCAS) provides excellent exposure to any candidate interested in Credit Risk: CCAR, DFAST, ICAAP, Basel RWA, and Economic Capital. The group is responsible for implementing key credit risk practices across Wholesale businesses, and ensuring consistency in methodologies within Wholesale Credit Risk. The team works across CIB, CB and AM, and is closely aligned with firm-wide partners including Credit Risk Management, Model Risk & Development, Finance, the Regulatory Capital Management Office, Technology and Reporting. We seek a candidate with strong skills in credit risk, quantitative finance, credit modeling and analytics, problem-solving, and communication.
WCAS’ areas of responsibility include Traditional Credit Product stress testing (CCAR/DFAST/ICAAP) and Basel RWA, developing the firm's authoritative wholesale credit risk parameter data set, reserve/allowance management, development and implementation of an economic credit capital model, the design and integration of credit and capital limits, risk grading methodology, and the provision of strategic advice and solutions to the originating businesses.
Perform data extraction, and advanced data mining techniques. Analyze and interpret financial data utilizing Excel, R, SAS and other statistical software to conduct comprehensive data quality analysis to support the development of complicated statistical model development and portfolio analyses. The candidate must be able to identify situations where data is insufficient, and develop creative work-arounds or other means to synthesize reliable data sources to support analytic function. Designing, developing, and preparing data for statistical models and segmentation for the bank's Wholesale (commercial real estate and commercial/industrial) businesses. Provide and present findings, model results, insights and recommendations to senior management and partners through MS Office (i.e., PowerPoint, Word, and Excel). Partner with internal teams to validate and implement models/segments/tools into system and to support business execution and/or analytics on revenue growth and loss control. Utilize graduate-level research and analysis skills to support and contribute to the team’s statistical analysis/modeling efforts. Develop models using linear regression, logistic regression, time series analysis/forecasting, multivariate analysis (i.e., clustering analysis, principal component analysis, discriminate analysis, etc.)
Education: Master's degree or significant financial institution experience in Statistics, Econometrics, Quantitative Finance, Operations Research, or equivalent quantitative field.
Experience: 0-3 years’ experience
· Education or experience must include:
1. Programming knowledge in R and/or SAS and P/L SQL. Experience with Python a plus;
2. Understanding of dataset aggregation and staging for reporting or modeling;
3. Graduate level analytical and problem solving skills with the ability to interpret complicated and large amounts of data with business insights to support business growth;
4. Model development techniques (regression, decision tree, and time series), pattern recognition and/or data mining experience;
5. Operating systems experience with UNIX and relational database knowledge such as ORACLE/TERADATA;
6. Microsoft Word, Excel, and PowerPoint utilizing pivot tables in Excel, to demonstrate business insight and conduct advanced analytics (experience with Tableau is a plus);
7. Solid report-writing skills.
Please note that J.P. Morgan will not accept unsolicited approaches or speculative CVs, nor will J.P. Morgan be responsible for any related fees, from Third Party Firms who are not preferred suppliers.
The firm invites all interested and qualified candidates to apply for employment opportunities.
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