Senior Data Scientist

pAt Johnson Johnson,we believe health is everything. Our strength in healthcare innovation empowers us to build aworld where complex diseases are prevented, treated, and cured,where treatments are smarter and less invasive, andsolutions 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 /ppAs 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. /ph3Job Function: /h3pData Analytics Computational Sciences /ph3Job Sub Function: /h3pData Science /ph3Job Category: /h3pScientific/Technology /ph3All Job Posting Locations: /h3pBeerse, Antwerp, Belgium, Cornellà de Llobregat, Barcelona, Spain, Madrid, Spain, Spring House, Pennsylvania, United States of America /ph3Job Description: /h3pThe RD 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. /ppThe 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 RD. /ph3Key Responsibilities /h3h3Machine Learning Modeling /h3ulliDevelop and deploy bML/AI models /b to support safety signal detection, dose selection, PK/PD modeling, toxicology insights, and translational interpretation. /liliImplement representation‑learning, predictive modeling, and multivariate analytics for datasets spanning in vivo studies, in vitro assays, exposure‑response data, and pathology information. /liliPartner with scientific SMEs to design modeling strategies aligned with PSTS decision points. /liliApply model governance, versioning, and validation standards consistent with RD AI practices. /li /ulh3Data Engineering Pipeline Development /h3ulliBuild and maintain scalable data pipelines that integrate PSTS‑relevant data sources (e.g., toxicology studies, PK/PD datasets, biomarker readouts, animal study repositories). /liliTransform raw experimental outputs into bstandardized, analysis‑ready, AI‑ready /b datasets using Python, R, and cloud‑native services. /liliContribute to harmonized scientific data models in collaboration with data engineering and ontology teams. /li /ulh3Scientific Domain Integration /h3ulliWork directly with toxicology, DMPK, and safety stakeholders to interpret scientific context and translate study designs into computational requirements. /liliApply understanding of mechanism‑based toxicology, exposure‑response concepts, and in vivo study structures to guide data transformations and modeling strategies. /liliEnhance cross‑study comparability via standardized terminologies, metadata practices, and quality checks. /liliCollaborate with PSTS functional experts, RD Data Science teams, and platform architects to ensure high‑quality, scalable data solutions. /li /ulh3Qualifications /h3h3Required /h3ulliAdvanced degree (MS or PhD) in bData Science, Computational Biology, Toxicology, Pharmacology, Biomedical Engineering, Computer Science /b , or related field. /lilib2+ years /b of experience applying machine learning and/or data engineering to scientific or biomedical datasets. /liliProficiency with bPython and/or R /b , SQL, and modern data engineering tooling (cloud computing, workflow orchestration, version control). /liliExperience with ML model development, evaluation, and deployment pipelines. /liliExperience working with biological, toxicology, PK/PD, or in vivo datasets. /li /ulh3Preferred /h3ulliExperience in safety sciences, ADME/DMPK, toxicogenomics, or biomarker analytics. /liliFamiliarity with scientific data formats (e.g., assay outputs, histopathology data, PK time‑course datasets). /liliExposure to ontologies, semantic technologies, or knowledge graph integration for scientific domains. /liliExperience with cloud‑based data architectures (AWS S3, Snowflake, Redshift). /liliUndersta