Principal Data Scientist - R&D DSDH - Preclinical Sciences & Translational Safety (PSTS)

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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, La Jolla, California, United States of America, Spring House, Pennsylvania, United States of America, Titusville, New Jersey, United States of America

Job Description:

Johnson & Johnson Innovative Medicine is recruiting for Principal Data Scientist - R&D DSDH - Preclinical Sciences & Translational Safety (PSTS)

The primary location for this position is open toSpring House, PA; Titusville, NJ; Spring House, PA; Cambridge, MA; San Diego, CA; Beerse, Belgium; Madrid, Spain; or Barcelona, Spain.

Candidate Interested in our Europe based locations, please apply to: R-069190

J&J Innovative Medicine develops treatments that improve the health of people worldwide. Research and development areas encompass oncology, immunology, neuroscience, cardiopulmonary and specialty ophthalmology. Our goal is to help people live longer, healthier lives. We have produced and marketed many first-in-class prescription medications and are poised to serve the broad needs of the healthcare market - from patients to practitioners and from clinics to hospitals. To learn more about Johnson & Johnson Innovative Medicine visit https://innovativemedicine.jnj.com/

Position Summary

The R&D Data Science organization is seeking a Data Scientist to 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. In this role, you will work closely with multidisciplinary teams-including toxicologists, PK/PD specialists, in vivo researchers, and safety professionals-to create AI-ready datasets, develop predictive models, and deliver analytical solutions that promote improved safety evaluations and facilitate translational research.

The successful candidate possesses hands-on experience in machine learning and data engineering, complemented by a solid understanding of toxicology, pharmacokinetics/pharmacodynamics (PK/PD), in vivo experimentation, and translational science. Additionally, this role requires strong communication and problem-solving skills, a passion for innovation, and the ability to adapt to evolving scientific challenges in pharmaceutical R&D.

Key Responsibilities

Machine Learning & Modeling

• Develop and deploy ML/AI models to support safety signal detection, dose selection, PK/PD modeling, toxicology insights, and translational interpretation.
• Implement representationlearning, predictive modeling, and multivariate analytics for datasets spanning in vivo studies, in vitro assays, exposureresponse data, and pathology information.
• Partner with scientific SMEs to design modeling strategies aligned with PSTS decision points.
• Apply model governance, versioning, and validation standards consistent with R&D AI practices.

Data Engineering & Pipeline Development

• Build and maintain scalable data pipelines that integrate PSTSrelevant data sources (e.g., toxicology studies, PK/PD datasets, biomarker readouts, animal study repositories).
• Transform raw experimental outputs into standardized, analysisready, AIready datasets using Python, R, and cloudnative services.
• Contribute to harmonized scientific data models in collaboration with data engineering and ontology teams.

Scientific Domain Integration

• Work directly with toxicology, DMPK, and safety stakeholders to interpret scientific context and translate study designs into computational requirements.
• Apply understanding of mechanismbased toxicology, exposureresponse concepts, and in vivo study structures to guide data transformations and modeling strategies.
• Enhance crossstudy comparability via standardized terminologies, metadata practices, and quality checks.
• Collaborate with PSTS functional experts, R&D Data Science teams, and platform architects to ensure high-quality, scalable data solutions.

Qualifications

Required

• Advanced degree (MS or PhD) in Data Science, Computational Biology, Toxico