JPMorgan Chase & Co. (NYSE: JPM) is a leading global financial services firm with assets of $2.6 trillion and 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 http://www.jpmorganchase.com/.
We are a team of statisticians, economists, and other analytics professionals, focused on statistical modeling within the Treasury group of the retail banking division of JPMorgan Chase & Co. We answer complex quantitative questions, using a wide variety of analytical techniques and leveraging one of the world’s largest data warehouse implementations. We do this in close tandem with our partners throughout the Finance organization at JPMorgan Chase, to help them make, execute, and assess critical business decisions.
This position will be focused on building models to answer questions and create forecasts for groups across Finance. Key responsibilities of the position will include:
Building statistical or econometric models for deposit growth, fee revenue, or other business drivers to for budgeting and to satisfy regulatory requirements (CCAR)
Forecasting the performance of branches or bankers, in order to optimize the branch network and staffing
Creating price elasticity models to optimize deposit and loan pricing
Identifying data anomalies and other cases when more investigation is required as part of the model-building process
Communicating results across a wide variety of audiences, including Finance partners, modeling teams in Risk, and Model Governance
Performing ad hoc statistical analysis to answer immediate business questions
In terms of specific background, you should have a PhD in a quantitative field of study, such as statistics or economics, with a sound foundation in one or more of the following analytical disciplines:
Linear and non-linear statistical modeling
Time series and forecasting
Panel (longitudinal) data analysis
You should also be comfortable manipulating data and building models using a statistical computing language, such as R, SAS, Python, Matlab, or Stata.
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