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 and governed data, including the use of embeddings, vector search, and semantic indexing.
- Familiarity with large language model orchestration, tool and function calling, and protocols for connecting agents to enterprise tools and data, such as the Model Context Protocol (MCP).
- Experience with prompt and context engineering, and with evaluating agent behavior through testing, tracing, observability, and guardrails using tools such as LangSmith or comparable evaluation frameworks.
- Understanding of responsible and trustworthy AI practices, including grounding, traceability, human oversight, and validation of AI and agentic systems for regulated (GxP) environments.
- Publications, patents, open-source contributions, or recognized technical leadership in semantic technologies, knowledge engineering, or data management.
Actual compensation will depend on a candidate's education, experience, skills, and geographic location. The anticipated wage for this position is
$126,000 - $204,600
Full-time equivalent employees also will be eligible for a company bonus (depending, in part, on company and individual performance). In addition, Lilly offers a comprehensive benefit program to eligible employees, including eligibility to participate in a company-sponsored 401(k); pension; vacation benefits; eligibility for medical, dental, vision and prescription drug benefits; flexible benefits (e.g., healthcare and/or dependent day care flexible spending accounts); life insurance and death benefits; certain time off and leave of absence benefits; and well-being benefits (e.g., employee assistance program, fitness benefits, and employee clubs and activities).Lilly reserves the right to amend, modify, or terminate its compensation and benefit programs in its sole discretion and Lilly's compensation practices and guidelines will apply regarding the details of any promotion or transfer of Lilly employees.
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