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.TheFP&A Statistical Modeler Analyst 2is an intermediate level position, part ofFP&A Model Development teamwhich 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 ofeconometric forecasting modelsfor keyBalance sheetandincome statementline 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.DevelopingChampion and Challenger modelsusing different time series forecasting methodologies to comply with SR 15-18 guidance.Development ofBenchmark modelsusing Industry data series to meet regulatory requirementsManagethemodel life-cyclefrom first-line of defense perspective and participate in Segmentation, Risk Identification, overlay discussions with Businesses and Finance teams.Responsiblefor writing model development documentation and partner with Model Risk Management (MRM) to address their feedback.Contributeto stakeholder conversations with Businesses, Finance, Treasury and Risk to seek their sign-offs on Champion models.Qualifications / skill sets:2-4years of relevant statistical /business experience in financial servicesStrong understanding of statistical techniques such asOrdinary Least Square regression (OLS),Fixed-effectPanel Regression, Error Correction Models, Seemingly Unrelated regression and Cointegration.Understanding ofMachine learning algorithmswill be a plusHands-on experience in programming and modeling usingSAS, Python and Ris preferred. Follow a culture of accountability and strict quality control of the data integrity and modeling processAbility to build key relationships with finance and business teamsMust be able to present technical matters in a way that is meaningful to the audienceEducation:Masters / PhD in quantitative discipline such as Statistics, Economics or related discipline------------------------------------------------------Job Family Group:Risk Management------------------------------------------------------Job Family:Risk Analytics, Modeling, and Validation------------------------------------------------------Time Type:------------------------------------------------------Citi is an equal opportunity and affirmative action employer.Qualified applicants will receive consideration without regard to their race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or status as a protected veteran.Citigroup Inc. and its subsidiaries ("Citi") invite all qualified interested applicants to apply for career opportunities. If you are a person with a disability and need a reasonable accommodation to use our search tools and/or apply for a career opportunity reviewAccessibility at Citi.View the "EEO is the Law" poster. View theEEO is the Law Supplement.View theEEO Policy Statement.View thePay Transparency Posting
