Vice President, Product Manager – Data Quality & AI Automation in the Data & Analytics Productivity & Transformation team
On-site · New York City, New York, United States
Job Summary
Vice President, Product Manager for Data Quality & AI Automation leads discovery and design for automation opportunities across the Data & Analytics organization, focusing on data quality, recurring reporting, and audience management. Builds and operates foundational data capabilities (metadata, semantic context, data quality controls) to enable governed, high-quality data assets at scale. Manages intake-to-graduation pilots, aligning with engineering, data owners, and business partners to move pilots into production with clear success metrics and adoption plans. Evaluates internal AI solutions and third-party offerings, defines production-readiness criteria, and scales successful approaches across the organization. Drives enablement, communications, and change management to ensure sustained adoption of new capabilities. Partners with cross-functional teams to define and implement scorecards, roadmaps, backlogs, and release plans, and to monitor performance against OKRs.
Required Qualifications
- 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
- 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
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