At U.S. Bank, we're passionate about helping customers and the communities where we live and work. The fifth-largest bank in the United States, we’re one of the country's most respected, innovative and successful financial institutions. U.S. Bank is an equal opportunity employer committed to creating a diverse workforce. We consider all qualified applicants without regard to race, religion, color, sex, national origin, age, sexual orientation, gender identity, disability or veteran status, among other factors.
U.S. Bank is seeking a quantitative analyst for the integrated financial modeling team. The incumbent will aid in development of a financial projection framework that integrates business requirements from the Credit, Treasury & Finance functions. Desired candidate will have experience with pre-provision net revenue (PPNR) modeling for retail loans and leases, small business, or wholesale portfolios.
This role will be part of a highly visible and dynamic quantitative risk function within U.S. Bank. Responsibilities include developing, validating, documenting, and implementing integrated forecasting models with a specific focus on PPNR and new business originations. Initial focus is the Bank’s installment portfolio but will expand to include other products. The analyst is also responsible for ensuring models are consistent with the Bank's risk management policies, procedures and practices by interfacing with staff in central financial modeling, treasury, credit, corporate finance, as well as model validation and audit services. In addition, the analyst will communicate statistical model functions and predictions to stakeholders to demonstrate effective risk management and compliance. Key deliverables include comprehensive written model technical documents, oral and written presentations, as well as written and commented code.
- Bachelor's degree in a quantitative field, and 10 or more years of experience in statistical modeling OR
- Master's or PhD degree in a quantitative field, and six or more years of experience in statistical modeling
- Master’s degree in a quantitative field (Econometrics, Statistics, Mathematics, or a similar field)
- Proficiency with R, SQL or Python
- Experience working with large datasets and building or validating advanced statistical models (e.g. logistic regression, time series models).
- Proficient in MS office suite products (Word, Excel and PowerPoint).
- Strong analytical and problem-solving skills, coupled with thoroughness and attention to detail.
- Ability to build relationships and collaborate with a wide range of individuals across various groups, including risk, finance, treasury, model validation, technology, and regulators.
- Strong oral and written communication skills, capable of addressing to both technical and non-technical audience.
- Ability to prioritize work, meet deadlines, work under pressure while balancing multiple priorities in a dynamic and complex environment
- Experience with stress test forecasting models
- Understanding of CCAR/DFAST, Basel A-IRB, CECL regulatory rules
- Experience with Essbase databases
- Experience with other statistical modeling software (SAS, Matlab, Python, etc.)
- Experience interpreting and applying complex financial regulations or accounting standards
- Experience working with financial institution regulatory agencies.
- Advanced knowledgeable of quantitative and qualitative risk factors, industry risks, competition risks, and risk management approaches
- Considerable knowledge of various regression techniques, parametric and non-parametric algorithms, times series techniques, and other statistical models, various model validation tests/methodologies