Lead Product Manager, Data & AI Integration, Technology Vmo (project Hire)

Disney Disney · Media · Burbank, CA +4

Lead Product Manager for Data & AI Integration within the Technology Vendor Management Office (VMO) at Disney. This role focuses on owning the strategy, roadmap, and execution of data and AI capabilities to optimize vendor management, enhance cost transparency, and support operational decision-making. The role involves translating business needs into product features, defining data requirements, and supporting the delivery of AI-assisted capabilities like forecasting and anomaly detection within established platforms.

What you'd actually do

  1. Own the product vision, strategy, and roadmap for data integration, analytics, and AI capabilities supporting vendor management optimization and cost transparency.
  2. Drive outcome based prioritization across initiatives (e.g., cost transparency, spend visibility, performance insights, workflow automation), balancing stakeholder needs, feasibility, and measurable impact.
  3. Translate business and operational needs into epics, user stories, acceptance criteria, and a prioritized backlog, partnering with delivery teams through refinement and release planning.
  4. Define and deliver product capabilities that enable vendor performance visibility, portfolio insights, and spend transparency, including clear success criteria and user outcomes.
  5. Define data requirements and integration needs, enabling reliable, analytics ready views across systems to support planning, forecasting, and performance tracking.

Skills

Required

  • 7+ years of technical product management or relevant experience
  • Owning product vision, strategy, and roadmaps for data integration, analytics, and AI-enabled solutions
  • Standardizing, automating, and optimizing vendor management and financial workflows
  • Designing and delivering capabilities for vendor performance management, portfolio optimization, and spend visibility
  • Supporting cost transparency and auditable financial processes
  • Establishing standardized data taxonomies and definitions
  • Defining data integrations and analytics to support planning, forecasting, and performance measurement
  • Identifying, prioritizing, and delivering business relevant, explainable AI use cases
  • Translating requirements into user stories, acceptance criteria, and prioritized backlogs
  • Measuring post-implementation impact
  • Advisory, communication, and stakeholder management skills
  • Supporting executive-level reporting and cross-functional collaboration
  • Understanding of financial controls, auditability, security

What the JD emphasized

  • AI capabilities
  • cost transparency
  • vendor management optimization
  • operational decision making
  • financial workflows

Other signals

  • AI-enabled solutions
  • AI capabilities
  • AI use cases