The R&D Data Science organization is recruiting for a Principal Data Scientist – Ontology Developer TDS to design, build, and govern the semantic frameworks that unify data across the development-to-delivery lifecycle for Therapeutics Development & Supply (TDS). You will translate scientific, technical, and operational concepts into well-structured ontologies, controlled vocabularies, and semantic models that enable interoperability, analytics, automation, and AI/ML applications across TDS. This role combines hands-on ontology engineering with product-oriented thinking, partnering closely with domain experts in Process Development, Manufacturing, Quality, Supply Chain, and Data Science teams. You will serve as a key technical contributor and creative problem solver with a strong understanding of semantic technologies and data modeling in life sciences or manufacturing domains.

Model, code, test, and publish ontology modules and controlled vocabularies supporting our TDS data ecosystems (e.g., process development, material attributes, equipment hierarchies, batch and product genealogy, quality signals, supply chain flows).

Translate domain knowledge from SMEs into OWL/RDF classes and properties, SKOS vocabularies, and SHACL constraints, following patterns from established ontology engineering practices.

Produce validated, versioned semantic models and API-ready outputs for integration into enterprise platforms.

Build mappings to enterprise canonical models, regulatory standards, and cross-functional ontologies.

Maintain accountability for components of the TDS ontology roadmap—setting scope, priority, use cases, and success metrics.

Define and enforce modeling guidelines, naming and versioning conventions, change control processes, and release/deprecation rules, similar to R&D ontology governance frameworks.

Implement data quality checks including coverage, conformance, identifier normalization, and provenance capture.

Produce automated validation reports and maintain SPARQL queries/tests.

Ensure TDS ontologies serve as foundational assets enabling knowledge graphs, data products, advanced analytics, and AI/ML workflows—mirroring the AI-readiness focus in technical roles.

Partner with Data Engineering and Data Architecture teams to embed semantic layers into data pipelines and metadata systems.

Support automation of classification, normalization, and entity linking using ML/NLP techniques.

Work with SMEs across Process Development, Manufacturing, Quality, Supply Chain, and Digital/Data Science to capture domain semantics and validate ontology structures.

Participate in broader enterprise communities of practice advancing data standardization, interoperability, and ontology reuse.

Engage stakeholders to understand business needs, communicate semantic designs, and ensure fit-for-purpose delivery—reflecting cross-functional expectations in data strategy roles.