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This listing doesn't post a salary. Most similar roles pay
$140,856–$199,175.
Based on 240 similar postings.
Employer
About Johnson & Johnson
Johnson & Johnson is a multinational corporation operating in three main segments: consumer health products, pharmaceuticals, and medical devices, known for brands like Tylenol, Band-Aid, and Janssen. Industry: Pharmaceuticals & Medical Devices
Johnson & Johnson currently has
56 open roles
on FindRole.
Listed pay typically runs
$117,000–$201,250
across 43 roles with salary data.
JOB TITLE: Senior Data Scientist - R&D DDSAI - Preclinical Sciences & Translational Safety
The R&D Data Science organization is seeking a Senior Data Scientist to drive impactful decisions within the Pharmaceutical Sciences & Translational Safety organization. You will collaborate with toxicologists, PK/PD specialists, and in vivo researchers to create AI-ready datasets and develop predictive models that improve safety evaluations and facilitate translational research. Key responsibilities include building scalable data pipelines, implementing representation-learning, and performing multivariate analytics on datasets spanning in vivo studies, in vitro assays, and pathology information. You will utilize Python, R, SQL, and cloud-native services like AWS S3, Snowflake, and Redshift. The role requires applying machine learning to complex biological problems, including mechanism-based toxicology, exposure-response concepts, and pharmacokinetics, to accelerate scientific discovery and support the development of next-generation therapies.
What does a Data Scientist earn?
Median $173200 from 344 postings across 66 companies.
Develop and deploy ML/AI models to support safety signal detection, dose selection, and PK/PD modeling.
Implement representation-learning and multivariate analytics for in vivo studies, in vitro assays, and pathology information.
Build and maintain scalable data pipelines to integrate toxicology studies, biomarker readouts, and animal study repositories.
Transform raw experimental outputs into standardized, analysis-ready datasets using Python, R, and cloud-native services.
Translate study designs into computational requirements by interpreting scientific context from toxicology and DMPK stakeholders.
Apply model governance, versioning, and validation standards consistent with R&D AI practices.
Contribute to harmonized scientific data models in collaboration with data engineering and ontology teams.
What we're looking for
Advanced degree (MS or PhD) in Data Science, Computational Biology, Toxicology, Pharmacology, Biomedical Engineering, Computer Science, or a 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 including cloud computing, workflow orchestration, and version control.
Experience with ML model development, evaluation, and deployment pipelines.
Experience working with biological, toxicology, PK/PD, or in vivo datasets.
Experience in safety sciences, ADME/DMPK, toxicogenomics, or biomarker analytics (preferred).
Familiarity with scientific data formats, ontologies, semantic technologies, or knowledge graph integration (preferred).
Experience with cloud-based data architectures (AWS S3, Snowflake, Redshift) and understanding of regulatory data standards (preferred).
Python
Scikit-learn
Pandas
NumPy
PyTorch
TensorFlow
AWS
Snowflake
Machine Learning
AI
Statistical Modeling
Version Control
Data Visualization
Bayesian Methods
Experimental Design
Business Intelligence
Data Science
Scientific Computing
Python
SQL
Databricks
Snowflake
RDKit
Machine Learning
CI/CD
Data Modeling
Data Engineering
OpenEye
Cheminformatics
Metadata Management
Data Quality
Version Control
FAIR Data Principles
Python
Mathematical Optimization
Mixed-Integer Programming
Simulation
Monte Carlo
Forecasting
Uncertainty Quantification
Stochastic Programming
Bayesian Decision Methods
Reinforcement Learning
AI/ML
Data Science
Version Control
Data Integration
Digital Twins