This role pays less than
85%
of similar roles. Most pay
$159,000–$227,462
— the shaded band above.
At the midpoint, this role pays about
$142k
versus about
$193k
for comparable roles.
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
52 open roles
on FindRole.
Listed pay typically runs
$117,000–$201,250
across 45 roles with salary data.
Senior Scientist, Data (AI) Scientist, Translational Safety joins the Data, Data Science & Artificial Intelligence organization to accelerate drug safety prediction across all stages of development. The role involves building predictive models and AI-reasoning frameworks to identify translational biomarkers and flag compounds with high risk using multimodal data from preclinical, clinical, and real-world evidence domains. Key responsibilities include designing, validating, and deploying machine learning solutions for mechanistic inference and clinical outcome prediction while integrating high-dimensional datasets like phenomics, transcriptomics, and proteomics. The position requires proficiency in Python, PyTorch or TensorFlow, and expertise in Transformers, CNNs, graph networks, causal inference, and foundation models. This role addresses the critical challenge of early safety detection to inform evidence-based go/no-go decisions, ultimately improving R&D productivity and ensuring patient safety by connecting discovery data with real-world clinical outcomes.
Design, build, and deploy AI/ML solutions to predict translational safety using multimodal data across discovery, preclinical, clinical, and real-world evidence domains.
Develop predictive models and reasoning frameworks for biomarker identification, mechanistic inference, and clinical outcome prediction.
Integrate high-dimensional datasets like phenomics, transcriptomics, and EHR to derive biological insights that de-risk safety signals.
Create and advance Foundation model connectors to link different R&D stages and support closed-loop learning.
Establish scientific validation by assessing biological plausibility and producing explainability packages for regulatory and cross-functional review.
Translate complex technical methods into clear communications for diverse stakeholders while maintaining reproducible code and documentation.
What we're looking for
A Ph.D. in Computational Biology, Bioinformatics, Biomedical Informatics, Computer Science, Statistics, Applied Mathematics, or a related quantitative discipline is preferred.
Candidates must have demonstrated experience applying AI/ML in life-sciences settings, typically 2+ years post-PhD or 3–5 years of relevant industry experience.
Candidates should possess domain expertise in translational science, biomarker discovery, safety assessment, or related drug-discovery applications.
A publication history or demonstrated contributions to top-tier conferences and journals is preferred.
Proficiency in Python and experience with AI frameworks like PyTorch or TensorFlow are required.
Deep knowledge of ML/DL methods, causal inference, and graph analytics is required.
Experience with multimodal representation learning, foundation models, LLMs/GraphRAG, and multi-omics data at scale is required.
Experience producing regulatory-acceptable model evidence or operationalizing models into decision workflows is preferred.
Python
R
Machine Learning
Genomics
Data Science
Cloud Computing
Large Language Models
Generative AI
Statistics
Computational Biology
Toxicology
Pharmacology
Molecular Profiling
Analytical Workflows
Dashboards
Python
R
Machine Learning
Data Science
High-Content Imaging
Cell Culture
Flow Cytometry
ELISA
Genomics
Cloud Computing
Large Language Models
Generative AI
Analytical Pipelines
Statistics
In Vitro Assays
Organ-on-Chip
Generative AI
Natural Language Processing
Machine Learning
Predictive Analytics
Data Engineering
Semantic Search
Workflow Automation
Responsible AI Frameworks
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