FP&A Statistical Modeler Intmd. Analyst, Citi

FP&A Statistical Modeler Intmd. Analyst

Citi

Location:

India, Gurgaon

Category:

Finance

Contract Type:

Not provided

Salary:

Not provided

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Job Description:

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.

Job Responsibility:

Development of econometric forecasting models for key balance sheet and income statement line items for capital and business planning purposes
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 understanding changes to quantitative requirements published by MRM in Model Testing Guidance and presenting the key changes to senior model development leads
Be a champion in addressing observations raised by MRM and Internal Audit in a quantitative manner by thinking out-of-box
Contribute to model convergence initiatives as part of firm’s Transformation journey for different businesses
Responsible to explain model results to front-office / FP&A teams during quarterly model runs under different scenarios provided by Economic Scenario Group
Responsible in seeking sign-offs on final selected models from key stakeholder such as Business heads, FP&A head, Treasury and Risk
Responsible for writing model development documentation and partner with Model Risk Management (MRM) to address their feedback

Requirements:

3-5 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
Masters / PhD in quantitative discipline such as Statistics, Economics or related discipline

Nice to have:

Understanding of Machine learning algorithms
Hands-on experience in programming and modeling using SAS, Python and R

Additional Information:

Job Posted:

March 22, 2025

Employment Type:

Fulltime

Work Type:

On-site work

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