Internship Credit Risk Modeling Jobs in California

We are looking for a strategic and results-driven quantitative leader to lead partnering with technology and lead automation initiatives within the Credit Risk Model Operations and Strategy team, as 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-as we migrate from SAS to Azure Databricks and Python.

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

We are seeking a strategic and technically skilled leader to partner with technology and drive automation within the Credit Risk Model Development and Decision Science (MDDS) team. This role will focus on enhancing model production, model monitoring, model implementation, reporting, and documentation capabilities through the development and maintenance of reusable code libraries, robust data source connections, containerized environments, testing and execution pipelines. The leader will also lead evaluation, selection, onboarding, and lifecycle maintenance of any third-party tools and platforms leveraged by the Credit Risk Model Development and Decision Science (MDDS) team. 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, 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 helping our team migrate our model infrastructure from SAS to Azure Databricks and Python. The emphasis is on building strong architectural foundations that support repeatable processes, improve efficiency, and facilitate automation. This role will partner with technology to lead the selection, onboarding, and maintenance of technology tools and platforms used in model operations and redesigning processes to be modular, scalable, and well-controlled.

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:

• Strong understanding of technology including fundamental software engineering principles, automation tools, cloud-based tools and infrastructure, database systems, and dashboard/visualization tools particularly in support of modeling /quantitative platforms.

• Exceptional leadership skills and ability to drive initiatives that span multiple teams and stakeholders.

• A mindset for collaboration, customer centricity, and risk management.

• Experience with risk modeling and data at large regulated financial institutions.

• Familiarity with AI-driven tools and automation platforms (e.g., Microsoft Copilot, Microsoft Power Platform) to streamline data operations and accelerate data delivery.

Innovation & Automation Leadership

• Design, build, and maintain reusable code libraries and frameworks to support model development, implementation, production and monitoring activities.

• Partner with technology to lead the selection, onboarding, and maintenance of technology tools and platforms used in model operations.

• Develop and manage automated data pipelines and containerized environments (e.g., Docker, Kubernetes) for scalable data processing, model operations.

• Implement orchestration tools (e.g., Apache Airflow) to streamline model operations workflows, reporting, and documentation.

Training & Enablement

• Develop and deliver onboarding programs for new team members, focusing on tooling, infrastructure, and best practices.

• Provide ongoing training and support to ensure effective use of automation tools and libraries.

• Foster a culture of continuous learning and innovation within the team.

Stakeholder Engagement

• Partner with model developers and model operations to assess and meet their technological needs in a timely and effective manner.

• Contribute to reporting and presentations for senior management and risk committees.

Basic Qualifications

• Bachelor's degree in a quantitative field, and 10 or more years of relevant experience
  OR

• MA/MS in a quantitative field, and six or more years of related experience
  OR

• PhD in a quantitative field, and five or more years of related experience

Preferred Skills/Experience

• Object-oriented python programming

• Databricks experience required in model development, model production, and/or model monitoring.

• Relational databases, SQL query optimization

• Code management and version control using Git

• AI/ML and generative AI approaches

• Strong project management and organizational skills

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