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

- **Company:** Johnson & Johnson
- **Location:** Spring House, PA, United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** 3d Models, Artificial Intelligence, Amazon S3, Data Analysis, Cloud Computing, Cloud Database, Computational Biology, Information Engineering, Data Visualization, R (Programming Language), Graph Database, Python (Programming Language), Machine Learning, SQL Databases, Management of Software Versions, Workflow Management Systems, Delivery Pipeline, Snowflake, Deep Learning, Information Technology, Software Version Control, Data Pipelines, Serverless Computing, Amazon Redshift
- **Published:** October 29, 2026
- **Apply:** https://www.jobmonkeyjobs.com/career/28145155/Senior-Data-Scientist-R-D-Ddsai-Preclinical-Sciences-Translational-Safety-Pennsylvania-Spring-House-1306

## About the Role

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., * 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)., 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

## 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., 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.