Pay
$118K–$193K is typical
The middle half of what similar US postings state. This one doesn’t list pay.
From 3,721 postings in the last year. Stated ranges, not salaries paid.
Spring House, Pennsylvania, United States of America
Actively hiring, first seen by us 6 days agoEmployment type in the employer’s posting: Full timeFrom the employer’s posting
Develop and deploy ML/AI models for safety signal detection, dose selection and PK/PD modeling
Build and maintain scalable data pipelines integrating toxicology, PK/PD and animal study data
Turn raw experimental outputs into standardized, AI-ready datasets using Python, R and cloud services
Translate study designs into computational requirements with toxicology and DMPK stakeholders
Apply model governance, versioning and validation standards
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).
Transform raw experimental outputs into standardized, analysis‑ready, AI‑ready datasets using Python, R, and cloud‑native services.
Work directly with toxicology, DMPK, and safety stakeholders to interpret scientific context and translate study designs into computational requirements.
Apply model governance, versioning, and validation standards consistent with R&D AI practices.
2+ years of experience applying machine learning and/or data engineering to scientific or biomedical datasets.
Experience in safety sciences, ADME/DMPK, toxicogenomics, or biomarker analytics.
Proficiency with Python and/or R, SQL, and modern data engineering tooling (cloud computing, workflow orchestration, version control).
Familiarity with scientific data formats (e.g., assay outputs, histopathology data, PK time-course datasets).
Experience with ML model development, evaluation, and deployment pipelines.
Experience with cloud‑based data architectures (AWS S3, Snowflake, Redshift).
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.
Actively hiringFirst seen by us 6 days ago, still listed, and not a repost of an earlier posting.
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
$118K–$193K is typical
The middle half of what similar US postings state. This one doesn’t list pay.
From 3,721 postings in the last year. Stated ranges, not salaries paid.
Day 6
Early. Similar jobs usually come down between day 7 and day 55.
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- 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.
- 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.
- 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).
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We file this job under Data Scientist, by the work the posting describes.
Data scientists use statistics and models to answer harder questions, such as whether a change worked, what demand will look like next quarter, or which customers are likely to leave.
Postings usually ask for Python or R, SQL, statistics, experiment design and common machine learning methods.
Unlike an ML engineer, a data scientist's main output is often an analysis or a first model, not software running inside a product.
Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-2051.
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.
Job Function:
Data Analytics & Computational SciencesJob Sub Function:
Data ScienceJob Category:
Scientific/TechnologyAll Job Posting Locations:
Beerse, Antwerp, Belgium, Cornellà de Llobregat, Barcelona, Spain, Madrid, Spain, Spring House, Pennsylvania, United States of AmericaJob Description:
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
Data Engineering & Pipeline Development
Scientific Domain Integration
Qualifications
Required
Preferred
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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Required Skills:
Preferred Skills:
Advanced Analytics, Business Intelligence (BI), Coaching, Collaboration, Critical Thinking, Data Analysis, Database Management, Data Privacy Standards, Data Reporting, Data Savvy, Data Science, Data Visualization, Econometric Models, Process Improvements, Technical Credibility, Technologically Savvy, Workflow Analysis
The anticipated base pay range for this position is :
Additional Description for Pay Transparency:
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Spring House, Pennsylvania, United States of America
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