Principal Scientist, Data Science (Translational Knowledge Engineering)
Johnson & Johnson
August 13, 2026
Remote friendly (Cambridge, MA)
United States
IT
Position Summary
- Design and implement semantic/knowledge architecture enabling AI-driven reasoning across the drug discovery and development lifecycle.
Mission
- Build the semantic foundation for AI systems to reason across discovery, preclinical, clinical, and post-marketing domains while preserving scientific meaning, provenance, and translational fidelity.
Key Responsibilities
- Semantic Architecture & Knowledge Modeling: Design/maintain enterprise knowledge models (discovery biology, toxicology, safety pharmacology, pathology, clinical development, pharmacovigilance, real-world evidence); create conceptual/logical/physical models for AI-enabled discovery.
- Ontology Engineering & Governance: Lead ontology strategy, development, governance, lifecycle management; curate/extend biomedical ontologies; establish governance/quality/semantic review; ensure consistency, provenance/traceability, and FAIR principles.
- Knowledge Graph & Reasoning Infrastructure: Build RDF-based knowledge graph architectures; develop semantic mappings/inference rules/reasoning frameworks; enable GraphRAG, semantic retrieval, AI agents, reasoning; ensure semantic interoperability.
- Translational Data Harmonization: Create semantic bridges across SEND, SDTM, ADaM, MedDRA, HPO, MONDO, SNOMED CT, FHIR, OMOP, Cell Ontology, Protein Ontology.
- Cross-Functional Leadership: Partner with stakeholders and engineering/platform teams; influence enterprise semantic strategy.
Required Qualifications
- PhD or Master’s in Biomedical Informatics, Bioinformatics, Computational Biology, Computer Science, Information Science, Knowledge Engineering, or related.
- 5+ years in biomedical informatics/semantic technologies/knowledge engineering/scientific data architecture.
- Experience designing ontology-driven systems in life sciences/healthcare/pharma R&D; across multiple drug discovery/development phases.
- Technical: ontology development/governance; knowledge representation; RDF/OWL/SHACL/SPARQL; semantic web technologies; enterprise ontology platforms; RDF graph architectures; semantic APIs; FAIR.
- Domain: familiarity with translational science/toxicology/safety pharmacology/clinical development/pharmacovigilance/regulatory standards; experience with SEND/SDTM/ADaM/MedDRA/HPO/MONDO/FHIR/OMOP.
Preferred Qualifications
- Experience building semantic foundations for AI/GraphRAG/agentic AI/scientific reasoning; LLM-based retrieval/reasoning; translational safety/efficacy/biomarker/mechanistic reasoning use cases; contributions to ontology standards/open-source ontologies/knowledge graph initiatives.
Benefits (time off)
- Vacation: 120 hours/year; Sick time: 40 hours/year (CO: 48; WA: 56); Holiday pay including floating holidays: 13 days/year; Work/Personal/Family time: up to 40 hours/year; Parental leave: 480 hours; Bereavement leave; Caregiver leave; Volunteer leave; Military spouse time-off.
- Design and implement semantic/knowledge architecture enabling AI-driven reasoning across the drug discovery and development lifecycle.
Mission
- Build the semantic foundation for AI systems to reason across discovery, preclinical, clinical, and post-marketing domains while preserving scientific meaning, provenance, and translational fidelity.
Key Responsibilities
- Semantic Architecture & Knowledge Modeling: Design/maintain enterprise knowledge models (discovery biology, toxicology, safety pharmacology, pathology, clinical development, pharmacovigilance, real-world evidence); create conceptual/logical/physical models for AI-enabled discovery.
- Ontology Engineering & Governance: Lead ontology strategy, development, governance, lifecycle management; curate/extend biomedical ontologies; establish governance/quality/semantic review; ensure consistency, provenance/traceability, and FAIR principles.
- Knowledge Graph & Reasoning Infrastructure: Build RDF-based knowledge graph architectures; develop semantic mappings/inference rules/reasoning frameworks; enable GraphRAG, semantic retrieval, AI agents, reasoning; ensure semantic interoperability.
- Translational Data Harmonization: Create semantic bridges across SEND, SDTM, ADaM, MedDRA, HPO, MONDO, SNOMED CT, FHIR, OMOP, Cell Ontology, Protein Ontology.
- Cross-Functional Leadership: Partner with stakeholders and engineering/platform teams; influence enterprise semantic strategy.
Required Qualifications
- PhD or Master’s in Biomedical Informatics, Bioinformatics, Computational Biology, Computer Science, Information Science, Knowledge Engineering, or related.
- 5+ years in biomedical informatics/semantic technologies/knowledge engineering/scientific data architecture.
- Experience designing ontology-driven systems in life sciences/healthcare/pharma R&D; across multiple drug discovery/development phases.
- Technical: ontology development/governance; knowledge representation; RDF/OWL/SHACL/SPARQL; semantic web technologies; enterprise ontology platforms; RDF graph architectures; semantic APIs; FAIR.
- Domain: familiarity with translational science/toxicology/safety pharmacology/clinical development/pharmacovigilance/regulatory standards; experience with SEND/SDTM/ADaM/MedDRA/HPO/MONDO/FHIR/OMOP.
Preferred Qualifications
- Experience building semantic foundations for AI/GraphRAG/agentic AI/scientific reasoning; LLM-based retrieval/reasoning; translational safety/efficacy/biomarker/mechanistic reasoning use cases; contributions to ontology standards/open-source ontologies/knowledge graph initiatives.
Benefits (time off)
- Vacation: 120 hours/year; Sick time: 40 hours/year (CO: 48; WA: 56); Holiday pay including floating holidays: 13 days/year; Work/Personal/Family time: up to 40 hours/year; Parental leave: 480 hours; Bereavement leave; Caregiver leave; Volunteer leave; Military spouse time-off.