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

Johnson & Johnson Innovative Medicine

About The Position

The R&D Data Science organization is seeking a Senior Data Scientist to use advanced machine learning, data engineering, and scientific domain expertise to support decisions and generate actionable insights within the Pharmaceutical Sciences & Translational Safety (PSTS) organization. The role works 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 improve safety evaluations and support translational research. The position calls for hands-on experience in machine learning and data engineering, alongside understanding of toxicology, pharmacokinetics/pharmacodynamics (PK/PD), in vivo experimentation, and translational science. Strong communication and problem-solving skills, innovation, and adaptability to evolving scientific challenges in pharmaceutical R&D are also important. The Senior Data Scientist develops and deploys models for safety signal detection, dose selection, PK/PD modeling, toxicology insights, and translational interpretation; builds scalable pipelines and standardized datasets; and collaborates with scientific, data engineering, ontology, and platform teams to deliver high-quality, scalable solutions. The role is based at one of the listed locations: Beerse, Belgium; Cornellà de Llobregat or Madrid, Spain; or Spring House, Pennsylvania, United States.

Requirements

Advanced degree (MS or PhD) in Data Science, Computational Biology, Toxicology, Pharmacology, Biomedical Engineering, Computer Science, or a related field.
At least 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 tools, including cloud computing, workflow orchestration, and version control.
Experience developing, evaluating, and deploying ML models and pipelines.
Experience working with biological, toxicology, PK/PD, or in vivo datasets.
Strong communication and problem-solving skills, a passion for innovation, and the ability to adapt to evolving scientific challenges in pharmaceutical R&D.

Nice To Haves

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

Responsibilities

Develop and deploy ML/AI models supporting safety signal detection, dose selection, PK/PD modeling, toxicology insights, and translational interpretation.
Implement representation learning, predictive modeling, and multivariate analytics for in vivo studies, in vitro assays, exposure-response data, and pathology information.
Partner with scientific subject-matter experts to design modeling strategies aligned with PSTS decision points.
Apply model governance, versioning, and validation standards consistent with R&D AI practices.
Build and maintain scalable data pipelines integrating PSTS-relevant sources, including toxicology studies, PK/PD datasets, biomarker readouts, and 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 with data engineering and ontology teams.
Work 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.
Improve cross-study comparability through 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.

Benefits

Consolidated retirement plan (pension) and savings plan (401(k)), subject to plan terms.
Eligibility to participate in the Company's long-term incentive program.
Vacation: 120 hours per calendar year, subject to applicable policy and date of hire.
Sick time: 40 hours per calendar year, with specified state-based amounts for Colorado and Washington.
Holiday pay, including floating holidays: 13 days per calendar year.
Work, Personal and Family Time: up to 40 hours per calendar year.
Parental leave: 480 hours within one year of the birth, adoption, or foster care of a child.
Bereavement leave: 240 hours for an immediate family member and 40 hours for an extended family member per calendar year.
Caregiver leave: 80 hours in a 52-week rolling period.
Volunteer leave: 32 hours per calendar year.
Military spouse time-off: 80 hours per calendar year.