VP Product Manager, AI Automation and Data Quality

Join the Data & Analytics Productivity & Transformation team and help reinvent how data and analysis drive outcomes across Chase. You’ll shape how analysts use AI-enabled automation to move faster, improve quality, and focus on higher-value work. This role offers high visibility, strong mobility, and hands-on ownership from discovery through production. 
If you enjoy partnering across product, engineering, and data leaders to deliver measurable impact, you’ll thrive here. 

Job summary 

As a Vice President, Product Manager – Data Quality & AI Automation in the Data & Analytics Productivity & Transformation team, you will lead persona-driven discovery and design for automation opportunities across the Data & Analytics organization, starting with data quality, recurring reporting, and audience management. 
You will build and operate foundational data capabilities—metadata, semantic context, and data quality controls—so our teams can create governed, high-quality data assets at scale. 
You will run an intake-to-graduation pipeline that turns promising pilots into production-ready solutions with clear success metrics and adoption plans.  
Working closely with engineering, data owners, and business partners, you will help analysts spend less time on repetitive work and more time on insights that matter. 

You will evaluate both internal AI solutions and third-party offerings using a consistent, criteria-driven framework and unified scorecards to enable fair comparisons and confident decisions.  
You will partner with automation and platform teams to integrate approved tools into standardized stacks and scale what works across the organization. 
You will also lead enablement, communications, and change management so new capabilities are adopted, used well, and sustained.

Job responsibilities 

• Establish and operate metadata, semantic, and data quality capabilities that enable governed, high-quality data assets. 
• Partner with automation teams to scale controls and automations that improve data quality, access management, and compliance outcomes. 
• Conduct structured discovery with targeted analyst personas to identify high-frequency workflows suited for automation. 
• Define problem statements, success criteria, and measurable outcomes for pilots and product releases. 
• Design and run inclusive, hands-on testing programs to gather real-world feedback from diverse analyst groups. 
• Lead intake, evaluation, and graduation for local solutions with firm-wide viability, using standardized scorecards and decision criteria. 
• Maintain portfolio roadmaps, backlogs, and release plans across the automation pipeline, balancing near-term wins with scalable foundations.
• Drive adoption through enablement, communications, and change management so new capabilities are understood and used effectively. 
• Partner with engineering and platform teams to define production-readiness criteria and move pilots into production safely and consistently. 
• Monitor performance against OKRs, iterate based on outcomes, and run regular partner check-ins to ensure delivery against objectives. 

Required qualifications, capabilities, and skills

• Proven experience evaluating, implementing, or managing AI-enabled analytics and automation solutions across a data/analytics organization. 
• Demonstrated ability to lead cross-functional product delivery from discovery through production (backlog, roadmap, releases, dependencies).
• Strong knowledge of metadata management, data quality controls/frameworks, semantic modeling, and data governance practices. 
• Strong analytical skills to define success criteria, measure impact, identify gaps, and drive iterative improvement.
• Experience influencing and aligning stakeholders across product, engineering, data owners, and business partners. 
• Demonstrated change management and enablement skills (training, communications, rollout planning, sustained adoption).
• Experience designing and executing structured pilots and user testing, including consistent evaluation criteria and scorecards. 
• Excellent written and verbal communication skills, including ability to explain technical concepts to mixed audiences and senior leaders.
• Demonstrated track record delivering measurable outcomes (productivity gains, cycle-time reduction, quality improvements, user satisfaction).
• Comfort operating in a fast-changing environment with strong learning agility and ability to adapt priorities as needed. 

Preferred qualifications, capabilities, and skills  

• Experience building enterprise-scale data quality programs, including controls, monitoring, and remediation workflows. 
• Experience commercializing internal solutions into reusable products and scaling adoption across multiple teams. 
• Familiarity with modern data/AI ecosystems and concepts (e.g., lakehouse patterns, semantic layers, NLP-enabled analytics). 
• Experience integrating approved AI assistants into analytics workflows with appropriate governance and risk considerations.
• Experience running communities of practice, enablement programs, or change networks for analytics organizations.