Comprehensive Capital Analysis Review (CCAR) is an annual regulatory submission to US Federal Reserve Board (FRB). It is used to ensure that institutions have robust, forward-looking capital planning processes that account for their unique risks and sufficient capital to continue operations throughout times of economic and financial stress. As part of CCAR, the Federal Reserve evaluates institutions' capital adequacy, internal capital adequacy assessment processes, and their plans to make capital distributions, such as dividend payments or stock repurchases.

The FP&A Statistical Modeler Analyst 2 is an intermediate level position, part of FP&A Model Development team which is responsible for developing econometric time-series models to project balance sheet and income statement for different products / businesses / geographies within the firm to support CCAR and QMMF.

Responsibilities:

• Development of econometric forecasting models for key Balance sheet and income statement line items for capital and business planning purposes. This includes the calculation of Net Interest Income (“NII”), Non-Interest Revenue (“NIR”), Interest Rate Exposure (“IRE”), and other associated interest rate risk metrics.
• Developing Champion and Challenger models using different time series forecasting methodologies to comply with SR 15-18 guidance.
• Development of Benchmark models using Industry data series to meet regulatory requirements
• Manage the model life-cycle from first-line of defense perspective and participate in Segmentation, Risk Identification, overlay discussions with Businesses and Finance teams.
• Responsible for writing model development documentation and partner with Model Risk Management (MRM) to address their feedback.
• Contribute to stakeholder conversations with Businesses, Finance, Treasury and Risk to seek their sign-offs on Champion models.

Qualifications / skill sets:

• 2-4 years of relevant statistical /business experience in financial services
• Strong understanding of statistical techniques such as Ordinary Least Square regression (OLS), Fixed-effect Panel Regression, Error Correction Models, Seemingly Unrelated regression and Cointegration.
• Understanding of Machine learning algorithms will be a plus
• Hands-on experience in programming and modeling using SAS, Python and R is preferred.  
• Follow a culture of accountability and strict quality control of the data integrity and modeling process
• Ability to build key relationships with finance and business teams
• Must be able to present technical matters in a way that is meaningful to the audience

Education:

• Masters / PhD in quantitative discipline such as Statistics, Economics or related discipline

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