Senior Data Scientist - R&D DDSAI - Preclinical Sciences & Translational Safety

Johnson & Johnson · Spring House

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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.

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The R&D Data Science organization is seeking a Senior 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 representation‑learning, predictive modeling, and multivariate analytics for datasets spanning in vivo studies, in vitro assays, exposure‑response 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 PSTS‑relevant data sources (e.g., toxicology studies, PK/PD datasets, biomarker readouts, animal study repositories).
• Transform raw experimental outputs into standardized, analysis‑ready, AI‑ready datasets using Python, R, and cloud‑native 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 mechanism‑based toxicology, exposure‑response concepts, and in vivo study structures to guide data transformations and modeling strategies.
• Enhance cross‑study 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, Toxicology, Pharmacology, Biomedical Engineering, Computer Science, or related field.
• 2+ years of experience applying machine learning and/or data engineering to scientific or biomedical datasets.
• Proficiency with Python and/or R, SQL, and modern data engineering tooling (cloud computing, workflow orchestration, version control).
• Experience with ML model development, evaluation, and deployment pipelines.
• Experience working with biological, toxicology, PK/PD, or in vivo datasets.

Preferred

• Experience in safety sciences, ADME/DMPK, toxicogenomics, or biomarker analytics.
• Familiarity with scientific data formats (e.g., assay outputs, histopathology data, PK time-course datasets).
• Exposure to ontologies, semantic technologies, or knowledge graph integration for scientific domains.
• Experience with cloud‑based data architectures (AWS S3, Snowflake, Redshift).
• Understanding of regulatory data standards (e.g., SEND, CDISC).

Why This Role Is Unique

This is a rare opportunity to grow in one of the world’s most ambitious and fastest-growing R&D Data Science organizations, shaping how PSTS data powers next‑generation therapies in the largest biomedical company on the planet. Your work will directly accelerate Johnson & Johnson’s scientific discovery, fuel AI innovation, and impact patients globally.

Johnson & Johnson is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, age, national origin, disability, protected veteran status or other characteristics protected by federal, state or local law. We actively seek qualified candidates who are protected veterans and individuals with disabilities as defined under VEVRAA and Section 503 of the Rehabilitation Act.

Johnson & Johnson is committed to providing an interview process that is inclusive of our applicants’ needs. If you are an individual with a disability and would like to request an accommodation, external applicants please contact us via https://www.jnj.com/contact-us/careers, internal employees contact AskGS to be directed to your accommodation resource.

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