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 www.jpmorganchase.com
Chase Consumer & Community Banking serves nearly 66 million consumers and 4 million small businesses with a broad
range of financial services through our 137,000 employees. Consumer & Community Banking Risk Management partners
with each CCB sub-line of business to identify, assess, prioritize and remediate risk. Our Risk Management professionals
work directly with Consumer Banking, Business Banking, Auto/Student Loan, Card and Commerce Services, Chase
Wealth Management and Mortgage Banking to minimize, monitor and control the probability of risk events and mitigate
the impact of risk events that do occur.
The Capital Risk Modeler is responsible for designing, developing and maintaining capital models for
Mortgage accounts. The position will document and communicate model results and insights to senior staff in
Consumer Risk, the Consumer LOBs, or Model Review and Governance.
More specifically, the Risk modeler will:
· Provide support for model development, implementation, performance monitoring and calibration.
· Handle a variety of analytic projects as well to support capital modeling efforts and business needs. Such projects
may include data research and leveraging capital models to solve business problems.
· Be responsible for compiling and documenting modeling and analytical results in an organized way.
The responsibilities include compiling appropriate data, applying multidimensional data aggregation, performing profile
analysis, and evaluating impacts using optimization/sequencing tools and/or classification and regression algorithms.
- Minimum of a Master's degree in a field that provides a strong background in finance, economics and statistical
methods. Ph.D. degree is strongly preferred.
- Minimum of 3-year experience in developing loan-level loss forecasting models and credit risk models within financial
- Possess a thorough understanding of the risk drivers of the models and their applications in estimating default and
prepayment risk and potential losses.
- Successful candidates will have had statistics or econometrics courses at the graduate level. Besides basic statistics,
the incumbent should have a solid theoretical knowledge of regressions with categorical dependent variable such as
logistic and probit and regressions with limited depended variable such as Tobit and survival modeling.
- Knowledge of linear and non-linear regressions and time series econometrics is also desired.
- Intermediate level of SAS skills, especially in modules such as SAS/Base, SAS/STAT, SAS/Macro, data mining and
simulation because SAS is heavily employed in creating data sets, ad hoc analyses and modeling.
- Handle projects independently with a minimum of oversight and supervision and should be able to make contributions
to the group's knowledge base by proposing creative and valuable ways for approaching problems and projects.
- Excellent communication skills are required, as the incumbent will frequently be called upon to make presentations to
the senior management and to write documents that describe work products in a clear manner.
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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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JPMorgan Chase is an equal opportunity and affirmative action employer Disability/Veteran.