This role pays less than
65%
of similar roles. Most pay
$136,546–$202,352
— the shaded band above.
At the midpoint, this role pays about
$159k
versus about
$169k
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 (Data Products, Integration & Analysis) leads the design, implementation, and evolution of scientific data products to support AI-enabled drug discovery and development. This role focuses on creating scalable, interoperable, and AI-ready data assets that connect discovery, preclinical, clinical, safety, and real-world evidence domains. The individual will establish data architecture, metadata frameworks, and integration strategies to enable semantic reasoning, knowledge graphs, GraphRAG, and agentic AI applications. Key responsibilities include building predictive AIML models for translational safety decision making and developing curated datasets and feature stores. Technical requirements include expertise in AWS-based platforms, data modeling, and governance, alongside familiarity with standards like SEND, SDTM, ADaM, MedDRA, FHIR, OMOP, DICOM, and omics data. The role solves complex problems in the drug development lifecycle by harmonizing heterogeneous scientific data to improve patient safety and research outcomes.
Lead the design and implementation of scientific data products to support AI-enabled drug discovery and development.
Create scalable, interoperable, and AI-ready data assets across preclinical, clinical, safety, and real-world evidence domains.
Establish data architecture, integration strategies, and metadata frameworks to support knowledge graphs and agentic AI applications.
Develop predictive AI/ML models to support data-driven translational safety decision-making.
Design integration frameworks to harmonize heterogeneous scientific data sources including SEND, SDTM, ADaM, and MedDRA.
Define and implement data quality standards, lineage, provenance, and FAIR data principles for scientific products.
Manage the implementation of scientific data products on AWS platforms in collaboration with engineering teams.
Translate complex scientific questions from stakeholders into scalable technical solutions and data products.
What we're looking for
Master’s or PhD in Computer Science, Data Engineering, Bioinformatics, Biomedical Informatics, Information Systems, Computational Biology, or a related scientific discipline.
5+ years of experience in scientific data engineering, data architecture, data products, or life sciences informatics.
Demonstrated experience designing and delivering enterprise-scale scientific data products.
Experience supporting drug discovery, development, clinical research, or pharmacovigilance organizations.
Experience developing predictive models within drug discovery, development, clinical research, or pharmacovigilance environments.
Expertise in data architecture, modeling, product design, metadata management, and governance.
Proficiency with AWS-based data platforms, including data lakes, lakehouses, and distributed processing.
Familiarity with scientific data standards such as SEND, SDTM, ADaM, and MedDRA.
Data Science
Artificial Intelligence
Generative AI
Agentic Workflows
Python
R
SQL
Cloud Platforms
Machine Learning
Digital Health
Workflow Automation
Real-World Data
Imaging
Omics
Analytics
Data Platforms