Quantitative Model Analyst

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, enabling the communities we support to grow and succeed in the right ways, all more confidently and more often—that’s what we call the courage to thrive. 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. Try new things, learn new skills and discover what you excel at—all from Day One. As a wholly owned subsidiary of U.S. Bank, Elavon is committed to building the platforms and ecosystems that help over 1.5 million customers around the world to achieve their financial goals—no matter what they need. From transaction processing to customer service, to driving innovation and launching new products, we’re building a range of tailored payment solutions powered by the latest technology. As part of our team, you can explore what motivates and energizes your career goals: partnering with our customers, our communities, and each other. We actively uphold transparent and fair hiring practices that support individual opportunity, inclusive culture, and career mobility across all levels of our organisation. We offer meaningful opportunities for growth, a culture of inclusion, and a strong commitment to transparency and integrity in everything we do. Job Description Job Description Quantitative Model Analyst About the Role • In this role, you will lead the development and implementation of expected loss forecasting models for Commercial Real Estate (CRE), Commercial Industrial (C&I), or Small Business portfolios, ensuring compliance with CECL, CCAR, and other regulatory requirements. • The ideal candidate is expected to have a good understanding of commercial portfolios and statistical methods, industry experience, U.S. banking regulations, excellent communication, and attention to detail as well as a strong background in predictive modeling. Your responsibilities • Model Development: • Develop expected loss forecasting models (PD/LGD/EAD) with best practice and document model methodology, selection evidence, model performance testing for validation and regulatory review. • Review and revise segmentation and modeling approach based on changes in business unit, portfolio or economic intuition. • Analyze model metrics (e.g., accuracy, stability), identify issues, and recommend improvements. • Coding: Use various programming languages (Python, SAS, SQL, R) in development and data analysis. Write and execute code in both local environment and cloud platforms (Azure or others). • Model Review: Provide effective challenges to existing and new models to identify the potential weak points and enhance model performance. • CCAR/CECL Submission: Provide support for stress testing (CCAR) submission and CECL process; document associated portfolios analysis, respective overlays for emerging risks and reasonableness analysis; respond questions from senior management and regulators in a timely manner. • Transformation: Leverage automation tools and Al to increase efficiency, reduce operational risk, and enhance usability and interpretability of results. Our requirements • Master's Degree or PhD in a quantitative field such as computer science, data science, mathematics, or statistics. • 5 or more years of experience in credit risk modeling and industry-standard approaches (e.g. PD, LGD, EAD). • Deep understanding of banking, financial metrics, and credit risk management. • Knowledge of banking regulation and requirements for stress testing and credit reserves. • Programming experience in Python (preferred) or similar statistical software (e.g. R, SAS) • Experience with cloud-based tools and infrastructure (Azure or others). • Strong analytical and problem-solving skills, coupled with thoroughness and attention to detail • Effective interpersonal, verbal, and written communication skills. • Ability to prioritize work, meet deadlines, and work under pressure and independently while balancing multiple priorities in a dynamic and complex environment. Optional • Experience in financial services, banking, and/or credit risk. • Experience working with large datasets and building or validating advanced statistical models (including regression and economic factor models) • Familiarity with automation using scripting tools (e.g. Bash) and low-code platforms (Microsoft Power Automate / Power Apps). • Exposure to machine learning concepts and their application in financial services. • Understanding of version control systems like Git. • Experience in Data visualization tools such as Power BI. This role requires working from a U.S. Bank location three (3) or more days per week. Accessibility We are committed to providing an inclusive and accessible recruitment experience. If you need adjustments at any stage of the application or hiring process, please contact your recruiter for guidance and support. Total Rewards U.S. Bank is committed to fair, equitable, and transparent compensation practices in line with local regulatory and legal requirements. Our total rewards approach is designed to attract, retain, and support top talent while ensuring equal pay for work of equal value. We offer a market-competitive compensation package that includes: • Clearly defined salary ranges aligned with industry benchmarks and internal equity standards. • Performance-based incentives for eligible employees (as defined by relevant plan rules), awarded through transparent, objective criteria that recognize both individual and company performance. • Inclusive equitable benefits that are accessible to all employees and focused around our 3 main pillars of financial wellbeing, health & wellness). • Continuous development opportunities including training, education support, and career progressio