Principal Scientist, Data Science (Translational Knowledge Engineering)

Position Summary

The Principal Translational Knowledge Architect & Graph Lead designs and implements semantic and knowledge architecture enabling AI-driven reasoning across the drug discovery and development lifecycle. Leads ontology development, knowledge graph design, semantic interoperability, and AI-ready knowledge representation.

Mission

Build the semantic foundation for AI reasoning across discovery, preclinical, clinical, and post-marketing domains while preserving scientific meaning, provenance, and translational fidelity.

Key Responsibilities

• Design/maintain enterprise knowledge models (discovery biology, toxicology, safety pharmacology, pathology, clinical development, pharmacovigilance, real-world evidence).
• Lead ontology strategy, development, governance, lifecycle management; ensure semantic consistency, provenance/traceability, and FAIR.
• Design RDF-based knowledge graph architectures; develop mappings/inference rules/reasoning frameworks.
• Enable GraphRAG/semantic retrieval/AI agents/next-gen translational intelligence via suitable knowledge representations.
• Develop semantic bridges across SEND, SDTM, ADaM, MedDRA, HPO, MONDO, SNOMED CT, FHIR, OMOP, Cell Ontology, Protein Ontology.
• Partner cross-functionally with Discovery, Preclinical Safety, Clinical Development, Pharmacovigilance, Data Science/Digital Health; collaborate with engineering/platform teams.

Required Qualifications

• PhD or Master’s in Biomedical Informatics, Bioinformatics, Computational Biology, Computer Science, Information Science, Knowledge Engineering, or related field.
• 5+ years in biomedical informatics/semantic technologies/knowledge engineering/scientific data architecture.
• Demonstrated ontology-driven knowledge system design; experience across multiple drug discovery/development phases.

Technical Expertise

• Ontology development/governance; knowledge representation; RDF/OWL/SHACL/SPARQL; Semantic Web technologies.

Preferred Qualifications

• Experience building semantic foundations for AI/GraphRAG/agentic AI/scientific reasoning; familiarity with LLM retrieval/reasoning.
• Support translational safety/efficacy/biomarker/mechanistic reasoning; contributions to ontology standards/open-source biomedical ontologies/knowledge graphs.

Benefits (time off)

• Vacation (120 hrs/yr), Sick time (40 hrs/yr; CO 48; WA 56), Holiday pay incl. Floating Holidays (13 days/yr), Work/Personal/Family Time (up to 40 hrs/yr), Parental Leave (480 hrs), Bereavement Leave (240 hrs immediate; 40 hrs extended), Caregiver Leave (80 hrs/52 weeks), Volunteer Leave (32 hrs/yr), Military Spouse Time-Off (80 hrs/yr).