This role pays less than
67%
of similar roles. Most pay
$139,518–$214,500
— the shaded band above.
At the midpoint, this role pays about
$159k
versus about
$177k
for comparable roles.
Based on 240 similar postings.
Employer
About Johnson & Johnson
Johnson & Johnson is a multinational corporation operating in three main segments: consumer health products, pharmaceuticals, and medical devices, known for brands like Tylenol, Band-Aid, and Janssen. Industry: Pharmaceuticals & Medical Devices
Johnson & Johnson currently has
46 open roles
on FindRole.
Listed pay typically runs
$117,000–$201,250
across 42 roles with salary data.
Principal Scientist, Data Science (Translational Knowledge Engineering) serves as a technical lead within the Innovative Medicine team to design and implement semantic knowledge architectures for AI-driven reasoning across the drug discovery and development lifecycle. The role involves building ontology models, designing RDF-based knowledge graphs, and establishing semantic interoperability to connect discovery biology, preclinical safety, clinical development, and real-world evidence into a unified framework. Key responsibilities include managing ontology governance, developing inference rules, and creating data mappings for systems like GraphRAG and agentic AI. The position requires expertise in OWL, SHACL, SPARQL, and Semantic Web technologies while utilizing standards such as SEND, SDTM, MedDRA, and FHIR. This role solves the challenge of maintaining scientific meaning and provenance while enabling automated reasoning across complex pharmaceutical data domains to accelerate the transition from lab to life.
Design and maintain enterprise knowledge models covering discovery biology, toxicology, safety pharmacology, and clinical development.
Lead ontology strategy, development, governance, and lifecycle management to ensure semantic consistency and FAIR data principles.
Develop RDF-based knowledge graph architectures and inference rules to support scientific decision-making and GraphRAG capabilities.
Create semantic bridges between heterogeneous data sources and industry standards like SEND, SDTM, MedDRA, and FHIR.
Build the semantic foundation required for AI systems to perform reasoning across the drug development lifecycle.
Partner with cross-functional teams of scientists, safety experts, and engineers to create high-quality knowledge assets.
Establish governance processes and quality standards for biomedical ontologies supporting translational safety and efficacy.
What we're looking for
Must possess a PhD or Master’s degree in Biomedical Informatics, Bioinformatics, Computational Biology, Computer Science, Information Science, Knowledge Engineering, or a related scientific discipline.
Requires 5+ years of experience in biomedical informatics, semantic technologies, knowledge engineering, or scientific data architecture.
Must have demonstrated experience designing ontology-driven knowledge systems in life sciences, healthcare, or pharmaceutical R&D environments.
Must possess deep expertise in ontology development and governance, including RDF, OWL, SHACL, SPARQL, and Semantic Web technologies.
Requires strong experience with enterprise ontology management platforms, RDF graph architectures, Semantic APIs, and FAIR data principles.
Must have experience working across multiple phases of drug discovery and development.
Requires familiarity with regulatory data standards such as SEND, SDTM, ADaM, MedDRA, HPO, MONDO, FHIR, or OMOP.
Must possess domain knowledge in areas such as translational science, toxicology, safety pharmacology, clinical development, or pharmacovigilance.
AWS
GraphRAG
Knowledge Graphs
Data Architecture
Data Engineering
Feature Stores
Metadata Management
SDTM
ADaM
MedDRA
FHIR
OMOP
DICOM
Data Governance
FAIR Data Principles
Distributed Data Processing
Ontologies
Knowledge Graphs
RDF
OWL
Semantic Web
RAG
Generative AI
Agentic AI
Metadata Management
Graph Databases
Data Science
Machine Learning
Deep Learning
Data Governance
Statistical Analysis
Time Series Analysis
Knowledge Graphs
RDF
OWL
Semantic Web
RAG
Ontologies
Taxonomies
Metadata Management
Data Governance
Machine Learning
Deep Learning
Artificial Intelligence
Graph Databases
Data Science
Statistical Analysis
Time Series Analysis
Data Visualization