Principal Scientist, Data Science (Translational Knowledge Engineering)

Position Summary

The Principal Translational Knowledge Architect & Graph Lead designs and implements the semantic and knowledge architecture that enables AI-driven reasoning across the drug discovery and development lifecycle. Serves as the scientific and technical lead for ontology development, knowledge graph design, semantic interoperability, and AI-ready knowledge representation. Works at the intersection of translational science, patient safety, biomedical informatics, and artificial intelligence to connect discovery biology, preclinical safety, clinical development, real-world evidence, and post-marketing safety into a unified reasoning framework. Partners with scientists, safety experts, data scientists, AI engineers, and platform teams to create knowledge assets that support GraphRAG, agentic AI, scientific reasoning, and next-generation translational intelligence capabilities.

Mission

Build the semantic foundation that enables AI systems to reason across discovery, preclinical, clinical, and post-marketing domains while preserving scientific meaning, provenance, and translational fidelity.

Responsibilities

• Semantic Architecture & Knowledge Modeling – Design and maintain enterprise knowledge models spanning:
  • Discovery biology, Toxicology, Safety pharmacology, Pathology
  • Clinical development, Pharmacovigilance, Real-world evidence
• Develop semantic frameworks that support translational reasoning across the R&D lifecycle. Create conceptual, logical, and physical knowledge models for AI-enabled scientific discovery.
• Ontology Engineering & Governance – Lead ontology strategy, development, governance, and lifecycle management. Curate and extend biomedical ontologies for translational safety and efficacy use cases. Establish governance processes, quality standards, and semantic review procedures. Ensure semantic consistency, provenance, traceability, and FAIR data principles.
• Knowledge Graph & Reasoning Infrastructure – Design RDF-based knowledge graph architectures, develop semantic mappings, inference rules, and reasoning frameworks. Define knowledge representations enabling GraphRAG, semantic retrieval, AI agents, and reasoning systems. Ensure semantic interoperability across data sources and standards.
• Translational Data Harmonization – Develop semantic bridges across major industry standards and ontologies (SEND, SDTM, ADaM, MedDRA, HPO, MONDO, SNOMED CT, FHIR, OMOP, Cell Ontology, Protein Ontology). Enable AI systems to traverse translational boundaries while preserving context.
• Scientific & Cross-Functional Leadership – Partner with stakeholders across Discovery, Preclinical Safety, Clinical Development, Pharmacovigilance, Data Science, and Digital Health. Collaborate with engineering teams and represent the organization in external standards and ontology communities when appropriate.

Qualifications

• Education – PhD or Master’s degree in Biomedical Informatics, Bioinformatics, Computational Biology, Computer Science, Information Science, Knowledge Engineering, or related scientific discipline.
• Experience – 5+ years 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. Experience across multiple phases of drug discovery and development.
• Technical Expertise – Deep expertise in ontology development and governance; knowledge representation (RDF, OWL, SHACL, SPARQL); Semantic Web technologies. Strong experience with enterprise ontology management platforms, RDF graph architectures, semantic APIs, and FAIR data principles.
• Domain Knowledge – Familiarity with translational science, Toxicology, Safety pharmacology, Clinical development, Pharmacovigilance; regulatory data standards. Experience with SEND, SDTM, ADaM, MedDRA, HPO, MONDO, FHIR, OMOP.
• Preferred Qualifications – Experience building semantic foundations for AI, GraphRAG, agentic AI, or scientific reasoning systems. Familiarity with LLM-based retrieval and reasoning architectures. Contributions to ontology standards or open-source biomedical ontologies. Leadership competencies include strategic thinking, communication, and cross-functional collaboration.

Company and Equal Opportunity

Johnson & Johnson is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, age, national origin, disability, protected veteran status or other characteristics protected by law. We actively seek qualified candidates who are protected veterans and individuals with disabilities. If accommodations are needed during the application process, please contact us via the Careers site.

Additional Information

The anticipated base pay range for this position is $117,000.00 - $201,250.00. This position offers eligible benefits, including retirement plans, time-off benefits, and other company policies as described in the pay transparency and benefits sections of the job posting.