Principal Scientist, Data Science; Translational Engineering

Job Function:

Data Analytics & Computational Sciences

Data Science

Scientific/Technology

Cambridge, Massachusetts, United States of America, Horsham, Pennsylvania, United States of America, Raritan, New Jersey, United States of America, Spring House, Pennsylvania, United States of America, Titusville, New Jersey, United States of America

About Innovative Medicine

Our expertise in Innovative Medicine is informed and inspired by patients, whose insights fuel our science-based advancements. Visionaries like you work on teams that save lives by developing the medicines of tomorrow.

Join us in developing treatments, finding cures, and pioneering the path from lab to life while championing patients every step of the way.

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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.

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
• 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
• Cell Ontology
• 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 when appropriate.

PhD or Master’s degree in:

• Biomedical Informatics
• Bioinformatics
• Computational Biology
• Computer Science
• Information Science
• Knowledge Engineering

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