Senior Ontologist, Biologics Discovery

Position Summary

As a Senior Ontologist for Biologics Discovery, you will design, build, test, publish, and govern semantic models that enable knowledge graphs, data products, search, analytics, and AI/ML across biologics workflows. You will partner with experimental scientists, computational biologists, data scientists, data engineers, platform teams, and IT to capture domain semantics and translate them into well-structured ontology modules, and you will set the semantic standards adopted across the team's data products. A core purpose of this work is to make our data agent-ready: agentic AI systems cannot reliably navigate, interpret, or act on scientific data without the shared vocabularies, relationships, and constraints that ontologies provide. You will build the semantic layer that lets agents ground their reasoning, retrieve the right data, and connect evidence across experiments, turning fragmented data into knowledge that both scientists and AI can act on. You will align biologics semantics to our enterprise semantic and governance standards, and collaborate with peer ontologists supporting Chemistry, Manufacturing, and Controls (CMC) in the Therapeutics Development & Supply organization so Discovery and downstream semantics stay aligned across the molecule lifecycle.

Key Responsibilities

Ontology Design, Release & Standards

• Model, code, test, and release validated, versioned ontology modules, controlled vocabularies, and mappings across biologics discovery domains, including antibody and protein engineering, assay metadata, molecular design, profiling, automation outputs, sequence/construct knowledge, and developability signals.
• Publish ontology packages as API-ready semantic assets consumable by knowledge graphs, data products, and AI/ML workflows.
• Partner closely with biologists, assay scientists, automation teams, and other domain experts to elicit, refine, and translate complex scientific concepts into computable semantic models, balancing scientific accuracy with practical usability and adoption.
• Express scientific and operational concepts as OWL/RDF classes and properties, SKOS vocabularies, SHACL constraints, and reusable design patterns, and establish the semantic standards adopted across the team's data products.

Knowledge Graphs & Semantic Integration

• Partner with data engineers to embed semantic layers into data pipelines, catalogs, and knowledge graph platforms.
• Model not only scientific entities and concepts, but also the processes, workflows, experimental activities, and decision points that generate and govern those entities, providing the context necessary for interoperability, provenance, and reuse.
• Enable entity linking, classification, normalization, provenance capture, and semantic search across biologics datasets, and ground agentic AI and retrieval workflows (e.g., knowledge graph and GraphRAG approaches) so agents can reliably navigate, interpret, and act on scientific data.
• Build and maintain semantic queries (e.g., SPARQL) and automated validation measuring semantic coverage, conformance, lineage, and completeness.

Enterprise & Cross-Functional Alignment

• Own semantic alignment with enterprise data governance and knowledge graph standards so Biologics Discovery builds on shared enterprise assets rather than duplicating governance.
• Harmonize terminology and models with peer ontologists supporting downstream development (CMC) across process, manufacturing, quality, and product lifecycle data, ensuring discovery-to-development handoffs remain traceable and interoperable.
• Drive consensus across scientific, informatics, and data teams on ontology scope, definitions, and adoption, representing Discovery's semantic needs in enterprise forums.
• Apply modeling guidelines, naming and versioning conventions, change management, deprecation rules, and semantic quality checks aligned to enterprise governance.

Qualifications

Required

• Advanced degree in Biomedical Informatics, Computational Biology, Bioinformatics, Data Science, Computer Science, or a related field.
• 3+ years in ontology engineering, knowledge modeling, taxonomy design, semantic standards, or knowledge graph development.
• Hands-on experience with versioning, change management, validation, and release workflows for ontology assets.
• Proficiency with OWL, RDF(S), SKOS, SHACL, SPARQL, and ontology design patterns.
• Experience with graph/semantic technologies (e.g., Neo4j, GraphDB, Amazon Neptune, Stardog, Protégé, RDF triple stores).
• Ability to translate scientific discussions into semantic models, validation tests, and delivery plans, and to align semantics across teams and partner organizations.
• Strong communication and cross-functional collaboration skills in a matrixed R&D environment.
• Working knowledge of how ontologies and knowledge graphs ground AI and agentic retrieval (e.g., semantic search, RAG, or GraphRAG approaches).

Preferred

• Experience in biologics, antibody design, protein engineering, assay biology, or high-throughput experimentation.
• Experience operating in a governed enterprise environment, preferably pharma or biotech, applying FAIR data principles and enabling interoperability across research and development handoffs.
• Experience integrating ontology assets into data pipelines, knowledge graphs, search, or agentic AI / GraphRAG tools.
• Familiarity with OBO Foundry ontologies, UMLS, GO, ChEBI, UniProt, HL7/FHIR, or CDISC.