Associate Director, Semantic & Knowledge Engineering (2 Openings)

About this role

Job Description Summary #LI-Hybrid

Reporting to the Executive Director, Semantic and Knowledge Engineering, the Associate Director, Semantic and Knowledge Engineering designs, builds, and governs enterprise semantic models, ontologies, taxonomies, business vocabularies, knowledge graphs, metadata services, and semantic APIs. The role enables AI, analytics, enterprise search, interoperability, retrieval-augmented generation (RAG), agentic AI, contextual search, and reasoning engines by embedding reusable semantic capabilities into products and workflows across Strategy, Platforms & Transformation.

The ideal location for this role is East Hanover but remote work may be possible (there may be some restrictions based on legal entity). Please note that this role would not provide relocation as a result. If associate is remote, all home office expenses and any travel/lodging to specific East Hanover for periodic live meetings will be at the employee’s expense. The expectation of working hours and travel (domestic and/or international) will be defined by the hiring manager. This position will require 10% travel.

There are 2 positions available.   Job Description Key Responsibilities :

Enterprise semantic modeling and knowledge engineering  

• Design, develop, and   maintain   enterprise ontologies, taxonomies, business   vocabularies , and semantic models that   establish   consistent meaning across products, data, and AI capabilities.  

• Build and enhance knowledge graphs, metadata services, and semantic APIs that support AI, analytics, enterprise applications, and commercial decision enablement.  

Semantic capabilities for AI and product workflows  

• Collaborate with Product, Applied AI, Analytics Engineering, and Platform Engineering teams to embed semantic capabilities into products, workflows, platforms, and reusable solution patterns.  

• Support semantic foundations for RAG, agentic AI, contextual search, reasoning engines, enterprise search, analytics, and interoperability initiatives.  

Metadata governance and   knowledge   lifecycle management  

• Implement metadata governance, semantic quality controls, stewardship practices, and lifecycle management for enterprise knowledge assets.  

• Promote quality, consistency, reuse, transparency, and governed evolution of semantic assets across the SPT ecosystem.  

Technology evaluation and architecture evolution  

• Evaluate emerging semantic technologies, standards, graph capabilities, and knowledge engineering approaches to inform the evolution of   the enterprise   knowledge architecture.  

• Contribute reusable engineering patterns and technical guidance that reduce duplication and improve scalability across semantic and knowledge engineering work.  

Technical mentorship and cross-functional collaboration  

• Mentor semantic and knowledge engineers while promoting reusable engineering patterns, technical excellence, disciplined documentation, and pragmatic implementation.  

• Communicate technical trade-offs, risks, dependencies, and   recommendations clearly   to product, engineering, analytics, AI, and business stakeholders.  

Essential Requirements:

• Education: Bachelor's degree in Computer Science , Information Science, Artificial Intelligence, Data Science, Bioinformatics, or a related discipline; advanced degree preferred.   •   6+ years of progressive experience in semantic technologies, knowledge engineering, metadata management, data/information architecture, data product engineering, or AI-enabling data platforms.  

• Hands-on experience designing and   maintaining   ontologies, taxonomies, controlled vocabularies, business glossaries, semantic models, RDF/OWL, SKOS, SPARQL, graph databases, knowledge graphs, metadata catalogs, data lineage, and semantic APIs/services.  

• Experience building semantic assets that enable AI grounding, RAG/ GraphRAG , enterprise search, contextual search, reasoning engines, analytics consistency, interoperability, and reusable product capabilities.  

• Working knowledge of data governance, stewardship models, provenance, quality controls, access/security controls, lifecycle management, privacy, and compliance expectations for enterprise knowledge assets.  

• Ability to translate complex business/domain concepts into reusable semantic models and partner effectively with Product, Applied AI, Analytics Engineering, Platform Engineering, Architecture, and business domain experts.  

• Strong engineering delivery discipline, including documentation, versioning, validation/testing of semantic assets, standards adherence, backlog execution, reusable patterns, and pragmatic implementation in agile/product teams.  

• Strong analytical, communication, stakeholder management, and collaboration skills, with the ability to explain semantic design choices, technical trade-offs, risks, and dependencies to technical and non-technical stakeholders.  

Desirable Requirements:

• Experience applying semantic technologies in pharmaceutical, healthcare, life sciences, commercial, clinical, medical, real-world data, or another regulated data environment.  

• Experience supporting generative AI, agentic AI, LLM-powered products, RAG/ GraphRAG , vector databases, graph-enhanced retrieval, knowledge graph embeddings, enterprise search, or AI-ready semantic layers.  

• Familiarity with FAIR data principles, master/reference data modernization, metadata platform roadmaps, ontology governance forums, stewardship operating models, and enterprise knowledge architecture practices.  

• Experience with cloud-based data and AI ecosystems, graph/vector tooling, semantic layer technologies, API-based semantic services, and integration with enterprise search or analytics platforms.  

• Experience mentoring junior semantic/knowledge engineers, shaping reusable engineering patterns, and contributing to technical standards or communities of practice.  

• Advanced degree or relevant certification in Compu

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