Senior AI Data Scientist, Translational Safety

Johnson Johnson; Titusville (NJ); On-site; USD 109,000 - 175,000; Full time

Benefits offered by this job

Vacation – 120 hours per year

Parental Leave – 480 hours per year

Comprehensive benefits

Job summary

Johnson & Johnson in New Jersey is seeking a Senior Scientist – Data (AI) Scientist, Translational Safety, to accelerate safety predictions across discovery and development using advanced AI/ML analytics.

You will integrate multimodal data, build predictive models, and collaborate with translational scientists to turn complex data into actionable insights that inform go/no-go decisions across R&D, while ensuring reproducible code and regulatory-ready documentation.

Qualifications

• Ph.D. preferred in Computational Biology, Bioinformatics, Biomedical Informatics, Computer Science, Statistics, Applied Mathematics or a related quantitative discipline; or equivalent experience.
• 2+ years post-PhD or ~3–5 years relevant industry experience.
• Track record in translational science, biomarker discovery, safety assessment or related drug-discovery applications.
• Strong programming proficiency, preferably Python, and experience with AI frameworks (PyTorch or TensorFlow).
• Deep knowledge of ML/DL methods (Transformers, CNNs, graph networks, self-supervised and multi-instance learning), causal inference and graph analytics.
• Experience with multimodal representation learning, foundation models, LLMs/GraphRAG and multimodal data fusion.
• Practical experience analyzing imaging/microscopy, multi-omics and real-world clinical data at scale.
• Excellent analytical thinking, scientific rigor, strong written and oral communication, collaborative cross-functional influence, and the ability to translate domain questions into robust AI solutions.
• Nice-to-have: prior experience producing regulatory-acceptable model evidence, operationalizing models into decision workflows, or building reusable model assets for R&D programs.

Responsibilities

• Design, build, validate and deploy AI/ML solutions for translational safety prediction using multimodal data across discovery, preclinical, clinical and real‑world evidence (RWE) domains.
• Develop predictive models and AI‑reasoning frameworks for translational safety, biomarker identification, mechanistic inference and clinical outcome prediction; evaluate traditional ML, deep learning, causal inference and foundation‑model approaches.
• Integrate heterogeneous, high‑dimensional datasets (e.g., high‑content imaging, phenomics, transcriptomics, proteomics, EHR, claims) to derive novel biological insights that de‑risk safety signals and inform portfolio decisions.
• Establish scientific validation: assess biological plausibility, benchmark model performance, produce explainability and validation packages suitable for regulatory and cross‑functional review.
• Prototype and advance Foundation‑model connectors and AI‑enabled reasoning to link discovery - preclinical - clinical - RWE and support closed‑loop learning across R&D.
• Collaborate closely with translational scientists, toxicology, clinical safety, PV, Knowledge Engineering, Data Products and external partners to prioritize use cases, operationalize models and drive productization.
• Communicate complex technical methods and results clearly to diverse audiences and stakeholders; maintain reproducible code, documentation and up‑to‑date versioned repositories.

Skills

Python

ML/AI frameworks

Multimodal learning

Data analytics

Communication

Education

Ph.D. in Computational Biology / Bioinformatics / Biomedical Informatics / Computer Science / Statistics / Applied Mathematics or related

Tools

PyTorch

TensorFlow

Transformers

Graph networks

Multimodal data fusion

Job description

Johnson & Johnson in New Jersey is seeking a Senior Scientist – Data (AI) Scientist, Translational Safety, to accelerate safety predictions across discovery and development using advanced AI/ML analytics.

You will integrate multimodal data, build predictive models, and collaborate with translational scientists to turn complex data into actionable insights that inform go/no-go decisions across R&D, while ensuring reproducible code and regulatory-ready documentation.