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CCB-Wholesale Loss Forecasting Model Analytics – Associate

Req #: 170097024_1
Location: Jersey City, NJ, US
Job Category: Accounting/Finance/Audit/Risk
Job Description:
JPMorgan Chase & Co. (NYSE: JPM) is a leading global financial services firm with operations worldwide. The firm is a leader in investment banking, financial services for consumers and small business, commercial banking, financial transaction processing, and asset management. A component of the Dow Jones Industrial Average, JPMorgan Chase & Co. serves millions of consumers in the United States and many of the world's most prominent corporate, institutional and government clients under its J.P. Morgan and Chase brands. Information about JPMorgan Chase & Co. is available at
Our Firmwide Risk Function is focused on cultivating a stronger, unified culture that embraces a sense of personal accountability for developing the highest corporate standards in governance and controls across the firm. Business priorities are built around the need to strengthen and guard the firm from the many risks we face, financial rigor, risk discipline, fostering a transparent culture and doing the right thing in every situation. We are equally focused on nurturing talent, respecting the diverse experiences that our team of Risk professionals bring and embracing an inclusive environment. 
Chase Consumer & Community Banking (CCB) serves consumers and small businesses with a broad range of financial services, including personal banking, small business banking and lending, mortgages, credit cards, payments, auto finance and investment advice. Consumer & Community Banking Risk Management partners with each CCB sub-line of business to identify, assess, prioritize and remediate risk. Types of risk that occur in consumer businesses include fraud, reputation, operational, credit, market and regulatory, among others
The CCB-Wholesale Loss Forecasting Model Analytics – Associate will join a team of highly skilled analysts to perform the Model Analytics function and will report to the wholesale model analytics lead. This is a highly visible role which must interact closely with model developers, model validation and model implementation. The role is expected to first understand mechanisms of all the sub models used for loss forecasting including PD, LGD, volume and EAD models. Then he / she will be working with rest of the team focusing on user testing, outcome analysis and eventually develop a challenging framework for all the models and assumptions used in the credit forecasting processes. Additionally, this role is expect to work together with rest of the team to build a 1-stop shop for all analytics need / what if analysis.
Key responsibilities include:
  • Develop deep subject matter expertise into all the models used in the Wholesale businesses supporting the credit forecasting processes (Budget, Allowances, ICAAP, CCAR and CECL)  
  • Perform outcome analysis and back testing to challenge existing and new models and assumptions
  • Develop a challenging framework to Identify model issues and limitations.
  • Work closely with model development, and the wholesale implementation teams.
  • Analyze complex business problems and able to quantify the issues with existing modeling framework or build ad-hoc solutions as needed
  • Adopt and implement best practices and standards for model development, testing, and validation.
  • Design sensitivity framework to serve business needs on what if analysis.
  • Conduct day to day monitoring of the Wholesale portfolios, conduct deep dive analysis and identify emerging trends 
  • Perform portfolio analysis to monitor portfolio performance and key risk factors, isolate issues and research on root causes.
  • 3-5 years of experience working in risk modeling, model validation, or advanced risk analytics. Preference will be given to candidates with prior experience in model development or model validation
  • Experienced in building Loss forecasting / capital reserve models.
  • Masters or Ph.D. in quantitative fields (economics, math, statistics, science and engineering)
  • Ability to leverage large complex data and models to synthetize and summarize findings and guide business actions
  • Strong programming skills in SAS,R, SQL and/or Python
  • Strong analytical/problem solving skills and attention to detail
  • Self-motivated and pro-active with proven ability to work accurately and under pressure to meet deadlines
  • Ability to summarize and explain clearly large and complex information in a simple and non-technical context
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