The Corporate and Commercial Quantitative Manager will be part of the central group that develops, implements, supports and reviews bank-wide models to forecast pre-provision net revenue for the Comprehensive Capital Analysis and Review (CCAR) stress test and other risk management processes. This role will work closely with Corporate Finance, Treasury and Payments business line management to understand appropriate data segmentation and key business drivers.
The Corporate and Commercial Quantitative Manager will lead a team whose primary purpose is to create, test, document, implement, and oversee usage of complex statistical models. This position will lead documenting the creation and testing of advanced statistical models and communicating such models to stakeholders within the Bank. Responsibilities include: collection of historic U.S. Bank, peer bank and industry data; performing statistical analyses using macroeconomic factors; model calibration while working closely with other members of the modeling team; assisting in analyzing and documenting model processes; converting technical model details into a format easily understood by senior management and regulators; staying well informed of current regulatory guidance to help ensure bank compliance; reviewing current models, model documentation and processes, and identifying opportunities for future model enhancements.
Proficiency with R
Knowledge of the corporate and commercial business including lending, equipment finance and small-ticket leasing, depository services, treasury management, capital markets, international trade services and other financial services to middle market, large corporate, commercial real estate, financial institution, non-profit and public sector clients
Experience with time series forecasting models
Excellent verbal and written communication skills (both the ability to communicate at the level of an academic journal and to explain complex ideas in non-technical language)
Ability to synthesize quantitative analysis into summary reports to aid management in decision making
Strong knowledge of financial and economic concepts
Strong mathematical, analytical and problem solving skills
Skill in organizing and manipulating large amounts of data
Experience with Essbase databases
Experience with other statistical modeling software (SAS, Matlab, Python, etc.)
Experiences with database languages (e.g. SQL)
Experience with and knowledge of stress testing requirements (CCAR and DFAST)