Principal Scientist, Data Science (Translational Knowledge Engineering)
About The Position
The Principal Translational Knowledge Architect & Graph Lead will be responsible for designing and implementing the semantic and knowledge architecture that enables AI-driven reasoning across the drug discovery and development lifecycle. This role will serve as the scientific and technical lead for ontology development, knowledge graph design, semantic interoperability, and AI-ready knowledge representation. Working at the intersection of translational science, patient safety, biomedical informatics, and artificial intelligence, this individual will help establish the semantic foundation required to connect discovery biology, preclinical safety, clinical development, real-world evidence, and post-marketing safety into a unified reasoning framework. The successful candidate will partner closely 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.
Requirements
- 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.
- 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, FAIR data principles
- Strong familiarity with one or more of: Translational science, Toxicology, Safety pharmacology, Clinical development, Pharmacovigilance, Regulatory data standards
- Experience working with: SEND, SDTM, ADaM, MedDRA, HPO, MONDO, FHIR, OMOP
Nice To Haves
- Experience building semantic foundations for AI, GraphRAG, agentic AI, or scientific reasoning systems.
- Familiarity with LLM-based retrieval and reasoning architectures.
- Experience supporting 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 who can engage effectively with scientists, clinicians, data scientists, engineers, and senior leadership.
- Ability to operate in ambiguous, highly cross-functional environments.
- Passion for advancing AI-enabled drug discovery and development through semantic and knowledge-driven approaches.
Responsibilities
- 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 supporting AI-enabled scientific discovery.
- Lead ontology strategy, development, governance, and lifecycle management.
- Curate and extend biomedical ontologies supporting translational safety and efficacy use cases.
- Establish ontology 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
- 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 when appropriate.
Benefits
- Vacation –120 hours per calendar year
- Sick time - 40 hours per calendar year; for employees who reside in the State of Colorado –48 hours per calendar year; for employees who reside in the State of Washington –56 hours per calendar year
- Holiday pay, including Floating Holidays –13 days per calendar year
- Work, Personal and Family Time - up to 40 hours per calendar year
- Parental Leave – 480 hours within one year of the birth/adoption/foster care of a child
- Bereavement Leave – 240 hours for an immediate family member: 40 hours for an extended family member per calendar year
- Caregiver Leave – 80 hours in a 52-week rolling period
- Volunteer Leave – 32 hours per calendar year
- Military Spouse Time-Off – 80 hours per calendar year
- Consolidated retirement plan (pension)
- Savings plan (401(k))
- Long-term incentive program
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What This Job Offers
Job Type
Full-time
Career Level
Principal
