Principal Scientist, Data Science; Translational Engineering

Position Summary

The Principal Translational Knowledge Architect & Graph Lead will design and implement the semantic and knowledge architecture that enables AI‑driven reasoning across the drug discovery and development lifecycle. This role serves as the scientific and technical lead for ontology development, knowledge graph design, semantic interoperability, and AI‑ready knowledge representation.

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.

• Design and maintain enterprise knowledge models spanning discovery biology, toxicology, safety pharmacology, pathology, clinical development, pharmacovigilance, and real‑world evidence.
• Develop semantic frameworks that support translational reasoning across the R&D lifecycle.
• Create conceptual, logical, and physical knowledge models supporting AI‑enabled scientific discovery.

• 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.

• Design RDF‑based knowledge graph architectures and related semantic technologies.
• Develop semantic mappings, inference rules, and reasoning frameworks supporting scientific decision‑making.
• Define knowledge representations enabling GraphRAG, semantic retrieval, AI agents, and reasoning systems.
• Establish semantic interoperability across heterogeneous data sources and standards.

• Develop semantic bridges across major industry standards and ontologies including SEND, SDTM, ADaM, MedDRA, HPO, MONDO, SNOMED CT, FHIR, OMOP, Cell Ontology, and Protein Ontology.
• Enable AI systems to traverse translational boundaries while preserving biological and clinical context.

• Partner with stakeholders across Discovery, Preclinical Safety, Clinical Development, Pharmacovigilance, Data Science, and Digital Health.
• Collaborate with engineering teams responsible for data products, pipelines, and AI platforms.
• Influence enterprise semantic strategy and represent the organization in external standards and ontology communities.

• PhD or Master’s degree in Biomedical Informatics, Bioinformatics, Computational Biology, Computer Science, Information Science, Knowledge Engineering, or a related scientific discipline.

• 5+ years in biomedical informatics, semantic technologies, knowledge engineering, or scientific data architecture.
• Experience designing ontology‑driven knowledge systems in life sciences, healthcare, or pharmaceutical R&D.
• Experience across multiple phases of drug discovery and development.

• Ontology development and governance
• Knowledge representation
• RDF, OWL, SHACL, SPARQL, and Semantic Web technologies
• Enterprise ontology management platforms, RDF graph architectures, semantic APIs, and FAIR data principles

• Translational science, toxicology, safety pharmacology, clinical development, pharmacovigilance, regulatory data standards.
• Experience with SEND, SDTM, ADaM, MedDRA, HPO, MONDO, FHIR, OMOP.

• Experience building semantic foundations for AI, GraphRAG, agentic AI, or scientific reasoning systems.
• Familiarity with LLM‑based retrieval and reasoning architectures.
• Support for translational safety, efficacy, biomarker, or mechanistic reasoning use cases.
• Contributions to ontology standards, open‑source biomedical ontologies, or scientific knowledge graph initiatives.

• Strategic thinker capable of translating scientific challenges into scalable knowledge architectures.
• Strong communicator engaging scientists, clinicians, data scientists, engineers, and senior leadership.
• Ability to operate in ambiguous, highly…