Principal Data Scientist – R&d Dsdh - Preclinical Sciences & Translational Safety (psts)
Johnson & Johnson Innovative Medicine
Cambridge, Massachusetts, United States of America
$117,000.00 - $201,250.00 py
On-site
Develop and deploy ml/ai models
Build scalable data pipelines
Integrate psts-relevant data sources
Johnson & Johnson is seeking a Principal Data Scientist focused on R&D in Preclinical Sciences and Translational Safety. The role requires expertise in machine learning and data engineering to support safety evaluations and translational research, emphasizing collaboration with multidisciplinary teams
Job Summary
Leverage advanced machine learning, robust data engineering techniques, and domain expertise to drive impactful decisions and generate actionable insights within the Pharmaceutical Sciences & Translational Safety (PSTS) organization.
Develop and deploy ML/AI models to support safety signal detection, dose selection, PK/PD modeling, toxicology insights, and translational interpretation.
Build and maintain scalable data pipelines that integrate PSTS-relevant data sources (e.g., toxicology studies, PK/PD datasets, biomarker readouts, animal study repositories).
Matching Summary
Match Score: 85
Johnson & Johnson is seeking a Principal Data Scientist focused on R&D in Preclinical Sciences and Translational Safety. The role requires expertise in machine learning and data engineering to support safety evaluations and translational research, emphasizing collaboration with multidisciplinary teams.
Salary
$117,000.00 - $201,250.00
Skills & Requirements
Must-have
Develop and deploy ML/AI models
Build scalable data pipelines
Integrate PSTS-relevant data sources
Proficiency with Python and/or R
Experience with ML model development
Nice-to-have
Experience in safety sciences
Familiarity with scientific data formats
Exposure to ontologies
Experience with cloud-based architectures
Understanding of regulatory data standards
Key Requirements
3+ years of experience
Advanced degree (MS or PhD)
Experience working with biological, toxicology, PK/PD, or in vivo datasets