Director Ontology & Knowledge Products

JOB SUMMARY The Director of Ontology & Knowledge Products owns BSWH's enterprise ontology and semantic and knowledge product portfolio. Ontology is the shared enterprise model of entities, definitions, relationships, business rules, and context that gives data consistent meaning. Knowledge products are reusable semantic, metadata, graph, curated knowledge, and retrieval assets that make that meaning usable by AI systems, analytics, operational workflows, and digital products.

This leader reports to the Vice President, Data & Knowledge and converts Data & Knowledge strategy and use-case needs into an executable portfolio roadmap, product requirements, semantic standards, lifecycle controls, delivery priorities, adoption plans, and value measures. The Director is accountable for semantic design, cross-domain alignment, provenance, versioning, data-product readiness, reuse, quality, and the operating model that turns ontology and knowledge assets into reusable enterprise capabilities.

The Director works closely with Data Product Owners, Data & Analytics Governance, Data Architecture, Data Engineering, AI Product, AI Engineering, AI Architecture, Clinical and Operational subject matter experts, Security, Privacy, Compliance, Legal, and external partners. Domain data owners remain accountable for source-data content and remediation; engineering teams remain accountable for pipelines, platforms, and runtime services; governance and clinical leaders retain formal policy and approval responsibilities.

This role ensures that AI systems, analytics, workflows, and digital products can interpret and use enterprise data consistently, with appropriate provenance, access controls, freshness, and traceability. This position can be based in our administrative building in Dallas, Texas or mostly remote with some

. ROLE ACCOUNTABILITIES Portfolio Strategy and Product Ownership - Own the enterprise ontology, semantic product, and knowledge product portfolio across priority AI, analytics, digital, and operational use cases. - Establish a consistent intake and prioritization process that assesses use-case value, domain coverage, data readiness, reuse potential, risk, dependencies, capacity, and investment needs. - Maintain a portfolio roadmap and backlog with clear product owners, value hypotheses, delivery plans, decision points, dependencies, risks, adoption measures, and retirement criteria. - Establish decision rights, prioritization criteria, intake processes, and operating routines for ontology design, enhancement, issue resolution, reuse, and cross-domain alignment. - Maintain alignment with the Vice President, Data & Knowledge on strategy, priorities, investment needs, risks, and executive-level progress.

Ontology Design, Semantic Modeling and Knowledge Architecture - Lead ontology design across enterprise domains, including entities, relationships, attributes, definitions, business rules, semantic standards, terminology mappings, and cross-domain relationships. - Ensure ontology designs reflect clinical, operational, financial, customer, and enterprise context through structured engagement with domain experts and data and product stakeholders. - Define semantic modeling standards that support AI reasoning, retrieval, analytics, knowledge graph enablement, workflow automation, and consistent interpretation of data across use cases. - Establish requirements for authoritative sources, provenance, lineage, freshness, temporal validity, access constraints, and traceability for important concepts and relationships. - Partner with ontology architects, data modelers, informaticists, and knowledge engineers to translate conceptual designs into implementable semantic products, data products, knowledge graphs, metadata, APIs, and retrieval layers.

Lifecycle, Quality and Operational Support - Own the lifecycle of ontology and knowledge products from discovery and design through implementation, validation, adoption, monitoring, enhancement, and retirement or replacement. - Create and maintain processes for change management, versioning, impact analysis, backward compatibility, release readiness, documentation, stakeholder review, deprecation, and migration. - Ensure ongoing refinement reflects AI use-case performance, retrieval or grounding results, data quality findings, operational feedback, new domain requirements, and evolving enterprise standards. - Define support models, service expectations, escalation paths, and resolution practices for semantic incidents, defects, ambiguity, conflicts, and change requests. - Track adoption, reuse, quality, source attribution, freshness, consumer impact, and value realization across AI use cases and enterprise consumers.

Data Product Owner Leadership and AI Data Readiness - Provide functional leadership to Senior Data Product Owners responsible for assigned data domains, while coordinating with their people managers when reporting relationships are outside this function. - Ensure Data Product Owners translate AI and business needs into clear data product requirements, data contracts, semantic acceptance criteria, quality expectations, latency requirements, access requirements, and remediation priorities. - Align domain-level data product roadmaps with ontology priorities so products are reliable, available, semantically consistent, traceable, and ready for their intended AI or analytical use. - Clarify that domain data owners remain accountable for source-data content, quality remediation, and operational decisions within their domains. - Resolve cross-domain prioritization, definition, quality, ownership, and dependency issues that affect AI use-case delivery or ontology reuse. - Partner with Data Engineering, Data Architecture, and source-system owners to ensure semantic requirements are reflected in data delivery and platform implementation.

Governance, Access and Responsible Use - Partner with the Director of Data & Analytics Governance to align data governance, analytics governance, stewardship, standards, policies, and controls with ontology and knowledge product needs. - Facilitate and document decisions about authoritative meanings, accepted definitions, data ownership, business rules, semantic conflicts, and appropriate use of governed data and knowledge assets. - Define requirements for provenance, traceability, auditability, access control, authorization propagation, privacy, and responsible AI across ontology and knowledge products. - Partner with Security, Privacy, Compliance, Legal, and clinical leaders to provide evidence and controls for appropriate use, review, approval, and ongoing monitoring. - Create enterprise playbooks, standards, reusable templates, and governance artifacts for ontology, data product, and knowledge product delivery.

Cross-Functional Delivery and Stakeholder Alignment - Serve as the primary ontology and knowledge product leader for AI use-case teams, ensuring teams understand semantic dependencies, design decisions, data readiness risks, delivery timelines, and support expectations. - Partner with AI Product Managers, the Principal AI Architect, the Director of AI Engineering, Data & Analytics, Clinical, Operational, and Journey stakeholders to ensure products meet real use-case needs. - Integrate ontology and knowledge product work into discovery, design, build, validation, launch, monitoring, and ongoing improvement routines for AI use cases. - Communicate priorities, design decisions, tradeoffs, risks, progress, adoption, quality, and support needs to leadership and cross-functional stakeholders. - Build a shared understanding of how ontology, data products, knowledge graphs, governance, retrieval, and AI systems work together to deliver trusted and maintainable capabilities.

Team Leadership and Operating Model - Build and lead the ontology, semantic product, knowledge engineering, and data product capabilities required by the Data & Knowledge function. - Define team structure, staffing needs, role expectations, career paths, delivery standards, technical review practices, succession plans, and capability development priorities. - Establish operating rhythms for intake, domain alignment, semantic review, product delivery, quality management, support, and executive reporting. - Coach team members and partners on product ownership, semantic quality, stakeholder engagement, communication, responsible use, and measurable impact. - Manage the function budget and external partners when applicable, and create a culture of stewardship, reuse, accountability, and continuous improvement.

OPERATING SCOPE AND INTERFACES - The Director is accountable for ontology and knowledge product strategy, semantic standards, lifecycle controls, cross-domain alignment, functional data-readiness criteria, reuse, quality, and evidence of value. - Domain data owners and stewards own source-data content, domain decisions, and remediation within their areas of responsibility. - Data Engineering, Data Architecture, and AI Engineering own pipelines, platforms, APIs, retrieval runtime services, and production reliability; the Director defines semantic and product requirements with those teams. - Data and Analytics Governance, Privacy, Security, Compliance, Legal, and clinical leaders retain formal policy, risk, privacy, security, and clinical approval responsibilities.

KEY SUCCESS FACTORS - Proven ability to establish, lead, or scale a production enterprise ontology, semantic layer, knowledge graph, metadata, information architecture, or knowledge product capability. - Strong understanding of semantic modeling, entity and relationship design, definitions, metadata, data quality, data product management, provenance, and knowledge graph enablement. - Ability to translate AI, analytics, clinical, operational, and business needs into ontology priorities, product requirements, design standards, delivery plans, and measurable outcomes. - Strong product ownership mindset, including roadmap management, backlog prioritization, stakeholder alignment, lifecycle management, reusable asset creation, adoption, and value measurement. - Ability to distinguish and coordinate ontology ownership, data product ownership, data governance, analytics governance, data architecture, data engineering, and AI engineering responsibilities. - Experience aligning data domains, semantic models, access controls, and governance practices so enterprise data can support AI reasoning, retrieval, analytics, and operational workflows. - Strong leadership through influence in a matrixed environment, including the ability to align domain experts, data teams, product teams, engineers, architects, governance leaders, and executive stakeholders. - Ability to balance delivery speed with semantic consistency, reuse, safety, reliability, traceability, governance, and long-term maintainability. - Comfort working in regulated, high-trust, or mission-critical environments where privacy, auditability, clinical or operational integrity, and customer trust are essential. - Strong communication skills, with the ability to explain ontology and semantic product concepts in practical terms for business, clinical, technical, and executive audiences.

Education - Bachelor's degree in Information Science, Data Science, Computer Science, Biomedical Informatics, Health Informatics, Engineering, Analytics, Business, or a related field. - Master's degree preferred.

Day Job Experience - 12+ years of experience in data, ontology, semantic modeling, enterprise information architecture, knowledge management, data product management, data governance, analytics, or related technology and data roles. - 5+ years of leadership experience, including direct leadership of teams, cross-functional programs, product portfolios, or major enterprise data and knowledge initiatives. - Experience designing, operating, or scaling ontology, semantic model, knowledge graph, metada