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 looking for a strategic and results-driven quantitative manager to lead initiatives within the Credit Risk Model Operations and Strategy team, part of the Model Development and Decision Science (MDDS) organization. This role will focus on redesigning and optimizing key processes across the model lifecycle—including implementation, production, and performance monitoring—to enhance efficiency and scalability. 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 | We create the future of credit risk management through data, analytics, and risk process innovation for our customers. Mission | We deliver data-driven information solutions to protect our stakeholders and inform the most significant financial decisions in the bank. Values | In addition to U.S. Bank core values, we prioritize collaboration, integrity, simplicity, and continuous learning. About the Role In this role, you will be responsible for designing systems and processes for model development, production, monitoring, and implementation. The models support loan portfolio stress testing (CCAR), the allowance for credit losses (ACL / CECL), counterparty risk, climate risk, and commercial risk rating scorecards. The ideal candidate will have hands-on experience in system design, a strong foundation in data science, and proficiency in quantitative programming languages. Key Activities This role centers on redesigning processes to be modular, scalable, and well-controlled. The emphasis is on building strong architectural foundations that support repeatable processes, improve efficiency, and facilitate automation. The processes in scope for this role include: Model development – build repeatable, standardized processes and tools for model development. Implementation – scalable tools to onboard models into production. Production – platforms for executing models for core production purposes. Monitoring – automated systems to track and assess model performance. Reporting – integrations and pipelines for dashboards and formatted output delivery. Core Competencies: Experience leading quantitative teams, strong understanding of predictive modeling techniques, and familiarity with credit risk data at large regulated financial institutions. Strong technology background including fundamental software engineering principles, automation tools, cloud-based tools and infrastructure, database systems, and dashboard/visualization tools. Exceptional leadership skills and ability to drive initiatives that span multiple teams and stakeholders. A mindset for collaboration, customer centricity, and risk management.