Director, Wealth and Wholesale Analytics & Customer Experience

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

Lead the analytics and customer experience insights strategy for Wealth and Wholesale, converting complex, multi-source data into decision products that drive revenue growth, client deepening, and business performance.

This role operates as a strategic partner to senior leadership, owning high-visibility insight platforms and shaping how data is translated into decisions across the organization. You will play a critical role in advancing AI-enabled, insight-driven decisioning, while establishing a consistent, trusted view of performance and client behavior.

What You’ll Do

• Define and lead the analytics and CX insights strategy, setting roadmaps, standards, and priorities across WCIB
• Own and scale executive insight products (e.g., Leadership Insights), ensuring they are decision-ready, highly adopted, and embedded in business workflows
• Serve as a trusted advisor to senior leaders, delivering concise, actionable insights that directly influence strategy and performance
• Integrate customer experience (VOC), behavioral, and operational data into a unified view of client and business performance
• Drive performance analytics across acquisition, deepening, retention, and portfolio management—linking insights directly to growth and efficiency outcomes
• Establish and reinforce trusted, consistent data and metrics across enterprise systems, improving transparency and confidence in decision-making
• Lead the shift from traditional reporting to scalable, insight-led products that enable faster, more effective decisions
• Advance AI-enabled insights, automation, and modern analytics capabilities to improve speed, scale, and impact
• Reduce duplication and increase reuse of analytics assets, driving consistency and efficiency across teams
• Partner across analytics, engineering, CX, and business teams in a matrixed environment to ensure alignment, scalability, and enterprise-ready solutions
• Build and develop high-performing analytics teams, fostering a culture focused on outcomes, ownership, and continuous improvement

What You Bring

Required Qualifications

• 12+ years leading analytics or data teams in complex environments
• Proven experience influencing senior executive decision-making through data and insights
• Track record of building and scaling analytics products and insights capabilities
• Strong grounding in data visualization, data ecosystems, and governance
• Exceptional executive communication and storytelling skills

Preferred Qualifications

• Experience in Wholesale/Commercial Banking, Payments, or Wealth
• Exposure to AI/ML or GenAI-driven analytics
• Experience operating in matrixed, enterprise organizations

Technical Leadership Expectations

Fluency across modern analytics and data ecosystems, with the ability to guide teams in delivering scalable, production-grade analytics solutions:

• Advanced data visualization (Power BI or similar), translating complex data into executive-ready insights
• Strong SQL and data engineering fundamentals, including data modeling and pipeline development
• Experience with cloud-based data platforms (e.g., Databricks, Snowflake, Synapse) and modern data architecture
• Familiarity with data movement and transformation tools (e.g., Alteryx, Azure Data Factory)
• Applied analytics mindset with working knowledge of Python, data science, and machine learning concepts
• Understanding of GenAI applications in analytics and decision-making
• Knowledge of data governance frameworks (data quality, lineage, metadata)
• Familiarity wi