{"id":144734,"url":"https://alion.io/job/novartis-associate-director-semantic-knowledge-engineering-2-openings","title":"Associate Director, Semantic & Knowledge Engineering (2 Openings)","company":{"id":5824,"name":"Novartis","domain":"novartis.com","url":"https://alion.io/company/novartis","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"B","score":82,"open_postings":71,"ghost_share":0,"stale_share":0.704,"repost_share":0.014,"time_to_fill_p50_days":27,"computed_at":"2026-10-30T05:48:23Z"}},"role":"Leadership","role_family":"Leadership","seniority":"head","employment_type":"full_time","work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"board_field","remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["United States"],"countries":["US"],"hiring_countries":["US"],"hiring_countries_total":1,"salary":{"min":176400,"max":327600,"currency":"USD","period":"year","gross":null,"usd_annual":327600},"salary_estimate":null,"experience_years_min":6,"visa_sponsorship":false,"relocation_package":false,"has_equity":true,"technologies":[{"name":"AI Agents","optional":false},{"name":"Embeddings","optional":false},{"name":"GraphRAG","optional":false},{"name":"Knowledge Graph","optional":false},{"name":"Platform Engineering","optional":false},{"name":"RAG","optional":false},{"name":"LLM","optional":true},{"name":"Machine Learning","optional":true},{"name":"Python","optional":true},{"name":"Time Series","optional":true}],"status":"live","first_seen_at":"2026-08-23T00:00:00Z","employer_posted_date":"2026-08-23","last_verified_at":"2026-10-30T18:01:25Z","board_verified":true,"closed_at":null,"days_open":68,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":68},"description":"Band\nLevel 5Job Description Summary\n#LI-HybridReporting 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.\nThe 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.\nThere are 2 positions available.\nJob Description\nKey Responsibilities:\nEnterprise semantic modeling and knowledge engineering\nDesign, develop, andmaintainenterprise ontologies, taxonomies, businessvocabularies, and semantic models thatestablishconsistent meaning across products, data, and AI capabilities.\nBuild and enhance knowledge graphs, metadata services, and semantic APIs that support AI, analytics, enterprise applications, and commercial decision enablement.\nSemantic capabilities for AI and product workflows\nCollaborate with Product, Applied AI, Analytics Engineering, and Platform Engineering teams to embed semantic capabilities into products, workflows, platforms, and reusable solution patterns.\nSupport semantic foundations for RAG, agentic AI, contextual search, reasoning engines, enterprise search, analytics, and interoperability initiatives.\nMetadata governance andknowledgelifecycle management\nImplement metadata governance, semantic quality controls, stewardship practices, and lifecycle management for enterprise knowledge assets.\nPromote quality, consistency, reuse, transparency, and governed evolution of semantic assets across the SPT ecosystem.\nTechnology evaluation and architecture evolution\nEvaluate emerging semantic technologies, standards, graph capabilities, and knowledge engineering approaches to inform the evolution ofthe enterpriseknowledge architecture.\nContribute reusable engineering patterns and technical guidance that reduce duplication and improve scalability across semantic and knowledge engineering work.\nTechnical mentorship and cross-functional collaboration\nMentor semantic and knowledge engineers while promoting reusable engineering patterns, technical excellence, disciplined documentation, and pragmatic implementation.\nCommunicate technical trade-offs, risks, dependencies, andrecommendations clearlyto product, engineering, analytics, AI, and business stakeholders.\nEssential Requirements:\nEducation: Bachelor's degree in Computer Science, Information Science, Artificial Intelligence, Data Science, Bioinformatics, or a related discipline; advanced degree preferred.\n6+ years of progressive experience in semantic technologies, knowledge engineering, metadata management, data/information architecture, data product engineering, or AI-enabling data platforms.\nHands-on experience designing andmaintainingontologies, taxonomies, controlled vocabularies, business glossaries, semantic models, RDF/OWL, SKOS, SPARQL, graph databases, knowledge graphs, metadata catalogs, data lineage, and semantic APIs/services.\nExperience building semantic assets that enable AI grounding, RAG/GraphRAG, enterprise search, contextual search, reasoning engines, analytics consistency, interoperability, and reusable product capabilities.\nWorking knowledge of data governance, stewardship models, provenance, quality controls, access/security controls, lifecycle management, privacy, and compliance expectations for enterprise knowledge assets.\nAbility 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.\nStrong engineering delivery discipline, including documentation, versioning, validation/testing of semantic assets, standards adherence, backlog execution, reusable patterns, and pragmatic implementation in agile/product teams.\nStrong 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.\nDesirable Requirements:\nExperience applying semantic technologies in pharmaceutical, healthcare, life sciences, commercial, clinical, medical, real-world data, or another regulated data environment.\nExperience 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.\nFamiliarity with FAIR data principles, master/reference data modernization, metadata platform roadmaps, ontology governance forums, stewardship operating models, and enterprise knowledge architecture practices.\nExperience 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.\nExperience mentoring junior semantic/knowledge engineers, shaping reusable engineering patterns, and contributing to technical standards or communities of practice.\nAdvanced degree or relevant certification in Computer Science, Information Science, Data Science, Artificial Intelligence, Bioinformatics, Knowledge Engineering, Ontology Engineering, ora relateddiscipline.\nNovartis Compensation Summary:\nThe salary for this position is expected to range between $176,400 and $327,600 per year.\nThe final salary offered isdeterminedbased on factors like, but not limited to, relevant skills and experience, and upon joining Novartis will be reviewed periodically. Novartis may change the published salary range based on company and market factors.\nYour compensation will include a performance-based cash incentive and, depending on the level of the role, eligibility to be considered for annual equity awards.\nUS-based eligible employees will receive a comprehensive benefits package that includes health,lifeand disability benefits, a 401(k) with company contribution and match, and a variety of other benefits. In addition, employees are eligible for a generous time off package including vacation, personal days,holidaysand other leaves.\nEEO Statement:\nThe Novartis Group of Companies are Equal Opportunity Employers. We do not discriminate in recruitment, hiring, training, promotion or other employment practices for reasons of race, color, religion, gender, national origin, age, sexual orientation, gender identity or expression, marital or veteran status, disability, or any other legally protected status. We strive to create an inclusive workplace that cultivates bold innovation through collaboration and empowers our people to unleash their full potential.\nAccessibility and reasonable accommodations\nThe Novartis Group of Companies are committed to working with and providing reasonable accommodation to individuals with disabilities. If, because of a medical condition or disability, you need a reasonable accommodation for any part of the application process, or in order to perform the essential functions of a position, please send an e-mail to  +1 (877)395-2339 and let us know the nature of your request and your contact information. Please include the job requisition number in your message.\nhttps://www.novartis.com/careers/careers-research/notice-all-applicants-us-job-openings\nSalary Range\n$176,400.00 - $327,600.00Skills Desired\nArtificial Intelligence (AI), Biostatistics, Business Value Creation, Change Management, Curious Mindset, Data Governance, Data Literacy, Data Quality, Data Science, Data Visualization, Deep Learning, Graph Algorithms, Learning Agility, Machine Learning (ML), Machine Learning Algorithms, Python (Programming Language), Stakeholder Engagement, Statistical Analysis, Time Series Analysis","description_format":"text","description_chars":8474,"description_truncated":false,"requirements":{"experience_years_min":6,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"bachelor","optional":false},"security_clearance":false,"languages":[]},"benefits":["Equity","Home office"],"hiring_locations":[{"name":"United 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