AI Product Owner

Key ResponsibilitiesVision & Strategy: Define and champion the enterprise ontology product vision, strategy, and multi-year roadmap aligned with overall corporate goals, growth initiatives, and operational efficiencies.Product Lifecycle Management: Own the end-to-end product lifecycle from ideation, requirements definition, and backlog prioritization through execution, launch, and continuous iteration.Governance & Stewardship: Establish and lead ontology governance processes, including change management, impact assessments, change review boards, and domain stewardship models with designated business owners.Data Integration & Engineering Collaboration: Partner closely with Data Engineering to map source systems, streamline data pipelines, and ensure continuous, reliable model updates.Validation & Quality Assurance: Define domain competency questions, validation tests, and quality standards to verify that models accurately reflect operational and analytical business needs prior to deployment.Metrics & Success Tracking: Establish and monitor metrics for adoption, model quality, definition coverage, mapping freshness, entity duplication, and lifecycle efficiency.AI & Semantic Layer Ownership: Own the semantic grounding layer for AI and agentic platforms, ensuring modern LLMs and intelligent agents operate on governed data models with embedded permissions, data lineage, and auditability.Stakeholder Management & Alignment: Collaborate across business units, technology teams, and executive leadership to gather requirements, communicate roadmaps, and build alignment.Requirement Translation: Translate complex business needs into clear user stories, acceptance criteria, process flows, and technical documentation.Domain Expertise & Documentation: Function as a subject matter expert across core business domains—identifying improvement opportunities, assessing existing capabilities, and maintaining clear product documentation.Requirements & QualificationsEducation: Bachelor’s degree in Computer Science, Information Systems, Data Analytics, or a related field (or equivalent practical experience).Experience: 5+ years of experience in product management, technical business analysis, or data product ownership.Specialized Experience: At least 2+ years focused on semantic modeling, ontology, taxonomy design, canonical data models, or knowledge graphs.Lifecycle Proven Track Record: Demonstrated experience taking data domains from fragmented or legacy sources through conceptual design, formal specification, testing, and production deployment.Technical Foundations: Working knowledge of formal knowledge representation principles (class hierarchies, properties, cardinality, inheritance, and controlled vocabularies) as well as SQL for data profiling and verification.Facilitation & Consensus Building: Proven ability to facilitate cross-functional modeling sessions, workshops, and domain alignment across matrixed organizations. xzbcwsv Communication: Excellent written, verbal, and executive presentation skills with a track record of driving cross-departmental alignment.

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