Advisor, Knowledge Engineering and Data Management

At Lilly, the work is demanding because patients are waiting. We unite caring with discovery to help make life better for people around the world, knowing that every decision, every detail, and every day matters. Headquartered in Indianapolis, Indiana, our over 50,000 employees around the globe take on complex challenges to discover and deliver life-changing medicines, strengthen how health is understood and managed, and support the communities we serve. This is hard, urgent, selfless work—but it’s work worth doing. If you’re driven by purpose and ready to bring your best to work that truly matters for patients, we invite you to join us. Company Overview: At Lilly, we unite caring with discovery to make life better for people around the world. We are a global healthcare leader committed to developing innovative medicines and healthcare solutions that improve outcomes for patients worldwide. We are looking for people who are determined to make life better for people around the world. Organization Overview: Delivery, Devices, and Connected Solutions (DDCS) sits within Lilly’s Product Research & Development organization. DDCS discovers, designs, develops, and commercializes patient-centric drug delivery systems, combination products, connected health solutions, and enabling technologies across the product lifecycle. The DDCS Data Science and Digital Transformation team builds trusted data, analytics, AI, and digital capabilities that connect scientific, engineering, quality, regulatory, manufacturing, and commercial knowledge. The team helps DDCS improve traceability, accelerate decision-making, and scale reusable data and knowledge assets. Position Overview: The Advisor, Knowledge Engineering and Data Management will lead the development and governance of semantic and data management capabilities for DDCS. This role will create, maintain, and govern ontologies, taxonomies, controlled vocabularies, metadata, and knowledge graph assets that transform fragmented technical information into connected, reusable, and trusted knowledge. This position is focused on the semantic and governance foundation required for search, traceability, analytics, knowledge discovery, and AI-enabled applications. Part of the role will involve hands-on agentic AI development, building agents and large language model applications that are grounded in DDCS knowledge assets. This work is anchored in a strong semantic and data management foundation, so that the AI systems the role helps build remain trustworthy, well-governed, and traceable to authoritative sources. Key Responsibilities: Knowledge engineering and semantic modeling: Design and maintain ontologies, taxonomies, controlled vocabularies, and semantic models for DDCS concepts such as device components, materials, formulations, test methods, requirements, design outputs, quality events, manufacturing processes, and connected-device data. Domain knowledge elicitation: Partner with device engineers, formulation scientists, quality professionals, regulatory experts, manufacturing stakeholders, and digital teams to translate domain knowledge into practical semantic models and reusable data assets. Data management and governance: Establish ownership, stewardship, metadata, lineage, change control, versioning, release management, and lifecycle practices for shared semantic and knowledge graph assets. Data quality and source alignment: Work with source-system owners to improve data definitions, source authority, semantic consistency, traceability, and quality across structured and unstructured information. Knowledge graph delivery: Design, build, and operate knowledge graph capabilities that integrate information from engineering systems, quality systems, PLM platforms, laboratory systems, manufacturing systems, document repositories, and other enterprise sources. Semantic enrichment and integration: Develop approaches for entity resolution, metadata harmonization, semantic enrichment, and relationship modeling across DDCS information assets. Search and AI enablement: Enable semantic search, graph-backed retrieval, entity extraction, and AI-ready knowledge structures grounded in governed DDCS data assets. Agentic AI development: Design, build, and evaluate AI agents and large language model applications, including retrieval augmented generation and graph based retrieval, that are grounded in governed DDCS knowledge assets and validated for regulated use. Validation and engineering practices: Apply version control, automated testing, validation, monitoring, and documentation practices appropriate for regulated environments and intended use. Communication and adoption: Communicate modeling decisions, governance expectations, assumptions, and limitations clearly to technical and non-technical stakeholders. Capability building: Mentor and guide scientists, engineers, analysts, and data professionals on practical use of semantic technologies and data management practices. Basic Requirements: Master’s degree in Information Science, Data Science, Computer Science, Engineering, Biomedical Informatics, Bioinformatics, or a related quantitative discipline, with a minimum of 5 years of relevant experience Experience developing ontologies, taxonomies, controlled vocabularies, semantic models, or knowledge graphs. Experience with semantic technologies such as RDF, OWL, SPARQL, SHACL, or equivalent frameworks. Python programming experience for data integration, automation, or analytics. Experience translating scientific, engineering, business, or data requirements into technical solutions. Demonstrated ability to collaborate with scientific, engineering, quality, regulatory, digital, and business stakeholders. Additional Preferences: PhD with a minimum of 2 years of relevant experience Experience implementing knowledge graphs, semantic platforms, metadata products, or governed data assets in production environments. Experience with data management practices such as stewardship, metadata management, lineage, data quality, data classification, or enterprise data governance. Experience with graph databases or semantic platforms such as Neo4j, Neptune, Stardog, GraphDB, or similar technologies. Experience in regulated industries such as pharmaceuticals, medical devices, healthcare, biotechnology, or manufacturing. Knowledge of pharmaceutical, medical device, or combination-product development processes, including design controls, DHF, DMR, requirements traceability, complaint handling, or UDI frameworks. Familiarity with GxP data integrity principles, ALCOA+, and life-science standards or vocabularies such as CDISC, IDMP, UNII, UCUM, or UDI/GUDID. Experience with enterprise metadata management, data catalog, governance, LIMS, ELN, PLM, quality management, or technical document management platforms. Experience supporting semantic search, retrieval, knowledge discovery, analytics, or AI-enabled applications using governed knowledge assets. Experience building AI agents or agentic workflows using frameworks such as LangGraph, LangChain, LlamaIndex, AutoGen, CrewAI, or Semantic Kernel. Experience with retrieval augmented generation (RAG) and graph based retrieval (GraphRAG) that grounds large language models in knowledge graphs