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CIB QR - Quantitative Research - Wholesale Credit Modeling - VP

Req #: 180011942
Location: Jersey City, NJ, US
Job Category: Accounting/Finance/Audit/Risk
Potential Referral Amount: 5000 US Dollar (USD)
JPMorgan Chase & Co. (NYSE: JPM) is a leading global financial services firm with assets of $2.4 trillion and operations worldwide. The Firm is a leader in investment banking, financial services for consumers and small businesses, commercial banking, financial transaction processing, asset management and private equity. A component of the Dow Jones Industrial Average, JPMorgan Chase & Co. serves millions of consumers in the United States and many of the worlds most prominent corporate, institutional and government clients under its J.P. Morgan and Chase brands. http://www.jpmorganchase.com.
 
We are looking for a candidate with very strong statistical background to work in the regulatory and economical domain. The candidate would be mainly responsible for developing models in area of wholesale credit risk, participating in all aspects of quantitative activities ranging from model research, development and prototyping to business support. An immediate assignment will be on loss forecasting model on our Fair Value Option portfolio secured by Commercial Real Estate (CRE).
Minimum education required: Ph.D. degree or equivalent in Statistics, Mathematical Finance, Mathematics, Operational Research or related quantitative field.
 
Minimum experience required: 3+ years of financial and/or economical modeling experience, with credit risk modeling on area of CMBS and/or CRE a strong plus.
 
Minimal skills required:
 
  • Strong analytic, quantitative and problem solving skills
  • Motivation to take initiative and solving problems independently
  • Capable of handling multiple projects/work streams simultaneously
  • Excellent data analysis and statistical modeling experience. This includes:
    • Theoretical and practical aspects of statistical inference
    • Generalized linear models, time series analysis, clustering, logistic regression
    • Ability to handle large amount of financial data, and data cleaning/filtering
    • Hypothesis testing and model selection, goodness-of-fit test
  • Hands on programming in Python and R/SAS
  • Excellent oral and written communication skills
 
And certain combinations of the following will be strong plus:
  • Credit risk and/or economical Capital experience
  • Econometrics and macro economical risk factors
 
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