Senior Quantitative Development Manager

At U.S. Bank, we’re on a journey to do our best. Helping the customers and businesses we serve to make better and smarter financial decisions and enabling the communities we support to grow and succeed.  We believe it takes all of us to bring our shared ambition to life, and each person is unique in their potential. A career with U.S. Bank gives you a wide, ever-growing range of opportunities to discover what makes you thrive at every stage of your career.

Try new things, learn new skills and discover what you excel at—all from Day One.##
** Job Description
** We are seeking a strategic leader in our Model Development & Decision Science (MDDS) team within Credit Risk Administration (CRA). This leader will be responsible for overseeing the development of expected loss forecasting models for our Commercial & Industrial portfolio in compliance with CECL, CCAR and other regulatory requirements (e.g. advanced approaches).
** About the CRA Team
** We are a highly dynamic and talented team which delivers on our mission through four pillars:
Customer, Process, Talent, and Data.  Vision I We create the future of credit risk management through data, analytics, and risk process innovation for our customers.  Mission I We deliver data-driven information solutions to protect our stakeholders and inform the most significant financial decisions in the bank.  Values I In addition to U.S. Bank core values, we prioritize collaboration, integrity, simplicity, and continuous learning.
** About the Role
** In this highly visible role, you will lead a team responsible for the development of Commercial & Industrial credit risk models in compliance with varied financial and regulatory requirements. You will be responsible for ensuring models are consistent with the Bank's risk management policies, procedures and practices by interfacing with staff in credit portfolio risk management, corporate finance, external reporting, as well as model validation and audit services.

You are expected to communicate statistical model functions and predictions to stakeholders to demonstrate effective risk management and compliance as well as to foster integrations of credit risk modeling into business as usual (BAU) activities.

Key deliverables include comprehensive written model technical documents, oral and written presentations, as well as fluent programming skills. The individual is expected to have a strong understanding of commercial portfolios and statistical methods, industry experience, excellent communication, partnership and attention to detail as well as a strong background in data science, predictive modeling, and technology and a strategic vision for the team including next generation model approaches (e.g. AI/ML, Gen AI).
** Key Activities
** The Wholesale modeling team activities encompass the following areas:  
• Data compilation and statistical analysis: analyze historical data, trends and recommend segmentation based on historic correlations to key economic variables  
• Business Unit partnership: review and revise segmentation and modeling approach based on changes in business unit, portfolio or economic intuition  
• Development:
Develop and document model methodology and selection evidence for validation and third party review  
• Coding:
Using various coding languages (Python, R, SAS, SQL) present final development code for validation and implementation  
• Monitoring and Model Performance: systematically track and report on the ongoing performance and stability of models.  
• CECL/CCAR Submission:
Documentation and presentation of portfolios analysis supporting modeled outputs, respective overlays for emerging risks and reasonableness analysis  
• Transformation - Leverage automation tools and Al to increase efficiency, reduce operational risk, and enhance usability and interpretability of results.
In addition, this role will oversee offshore resources to supplement and support the U.S.

-based team on all areas above.

** Core Competencies*
* • Experience leading quantitative teams, strong understanding of predictive modeling techniques, and familiarity with credit risk data at large regulated financial institutions.  
•…