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.
• 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 transformation initiatives that span multiple teams and stakeholders.
• A mindset for collaboration, customer centricity, and risk management.

Basic Qualifications
- Bachelor’s degree (MA/MS/PhD strongly preferred) and eight or more years of relevant experience
- Four or more years of experience leading a quantitative modeling team
 

Preferred Skills

• Master's Degree or PhD in a quantitative field such as computer science, data science, mathematics, or statistics.
• 10 or more years of experience in a leadership role in model development/implementation, software engineering, or related area.
• Strong familiarity with 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.
• Demonstrated success attracting talent, building, and leading teams of model developers or analysts in similarly technical fields.
• Excellent executive presence and verbal and written communication skills.
• Ability to build strong relationships with a wide range of individuals from risk, finance, model validation, technology, and regulators
• Strong analytical and problem solving skills, coupled with thoroughness and attention to detail
• Ability to prioritize work, meet deadlines, work under pressure and independently while balancing multiple priorities in a dynamic and complex environment
• Strong analytical, organizational, problem-solving, and project-management skills.
• Experience working with large datasets and building or validating advanced statistical models (including regression and economic factor models)
• Extensive experience in building credit models for commercial exposures; Experience interpreting and applying Basel A-IRB, CCAR/DFAST, CECL regulatory rules and experience working with financial institution regulatory agencies; Experience working with internal model validation and model risk management
• Programming experience in Python, SAS (Base, STAT, and/or Enterprise Guide) Experience with MS Word, Excel, and PowerPoint
• Automation using Bash/shell scripting and orchestration tools like Apache Airflow.
• Relational databases, SQL query optimization.
• Code management and version control using Git.
• Cloud-based solution deployment (AWS or Azure) and containerization/orchestration tools (e.g. Docker, Kubernetes).
• Al/ML and GenAI approaches.
• Microsoft Power Automate/ Power Apps.
• PowerBI or other visualization dashboards

LOCATION EXPECTATIONS:  This role requires working from a U.S. Bank Location three (3) or more days per week.