Principal Scientist, Data Science (translational Knowledge Engineering)

Johnson & Johnson Johnson & Johnson · Pharma · Spring House, PA +7

The Principal Translational Knowledge Architect & Graph Lead will design and implement the semantic and knowledge architecture for AI-driven reasoning across the drug discovery and development lifecycle. This role focuses on ontology development, knowledge graph design, and semantic interoperability to create a unified reasoning framework connecting various stages of R&D. The goal is to build knowledge assets supporting GraphRAG, agentic AI, and scientific reasoning capabilities within the healthcare domain.

What you'd actually do

  1. Design and maintain enterprise knowledge models spanning: Discovery biology, Toxicology, Safety pharmacology, Pathology, Clinical development, Pharmacovigilance, Real-world evidence
  2. Lead ontology strategy, development, governance, and lifecycle management.
  3. Design RDF-based knowledge graph architectures and related semantic technologies.
  4. Develop semantic bridges across major industry standards and ontologies, including: SEND, SDTM, ADaM, MedDRA, HPO, MONDO, SNOMED CT, FHIR, OMOP, Cell Ontology, Protein Ontology
  5. Partner with stakeholders across Discovery, Preclinical Safety, Clinical Development, Pharmacovigilance, Data Science, and Digital Health.

Skills

Required

  • PhD or Master’s degree in: Biomedical Informatics, Bioinformatics, Computational Biology, Computer Science, Information Science, Knowledge Engineering, Related scientific discipline
  • 5+ years of experience in biomedical informatics, semantic technologies, knowledge engineering, or scientific data architecture.
  • Demonstrated experience designing ontology-driven knowledge systems in life sciences, healthcare, or pharmaceutical R&D environments.
  • Experience working across multiple phases of drug discovery and development.
  • Ontology development and governance
  • Knowledge representation
  • RDF
  • OWL
  • SHACL
  • SPARQL
  • Semantic Web technologies
  • Enterprise ontology management platforms
  • RDF graph architecture

What the JD emphasized

  • semantic foundation

Other signals

  • designing and implementing the semantic and knowledge architecture that enables AI-driven reasoning
  • semantic foundation required to connect discovery biology, preclinical safety, clinical development, real-world evidence, and post-marketing safety into a unified reasoning framework
  • knowledge assets that support GraphRAG, agentic AI, scientific reasoning, and next-generation translational intelligence capabilities