Principal Scientist, Data Science (Translational Knowledge Engineering)

At Johnson & Johnson, we believe health is everything. Our strength in healthcare innovation empowers us to build a world where complex diseases are prevented, treated, and cured, where treatments are smarter and less invasive, and solutions are personal. Through our expertise in Innovative Medicine and MedTech, we are uniquely positioned to innovate across the full spectrum of healthcare solutions today to deliver the breakthroughs of tomorrow, and profoundly impact health for humanity. Learn more at jnj.com

As guided by Our Credo, Johnson & Johnson is responsible to our employees who work with us throughout the world.  We provide an inclusive work environment where each person is considered as an individual.  At Johnson & Johnson, we respect the diversity and dignity of our employees and recognize their merit.

Job Function:

Data Analytics & Computational Sciences

Job Sub Function:

Data Science

Job Category:

Scientific/Technology

All Job Posting Locations:

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

Job Description:

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.
Learn more at https://www.jnj.com/innovative-medicine

Position Summary

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.

Key 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 supporting AI-enabled scientific discovery.

 

Ontology Engineering & Governance

• 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.

Knowledge Graph & Reasoning Infrastructure

• 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 interoperab