Senior Translational AI Scientist – Safety & Biomarkers

Johnson & Johnson Innovative Medicine; Cambridge (MA); On-site; USD 109,000 - 175,000; Full time

Job summary

Johnson & Johnson is seeking a Senior Scientist – Data (AI) Scientist, Translational Safety, to accelerate drug safety prediction across discovery and development using advanced AI/ML analytics and multimodal data.

The role develops predictive models to identify translational biomarkers, support risk-minimization decisions, and connect discovery to real-world evidence. Collaboration with scientists across pharmacology, toxicology and data engineering is essential.

Qualifications

• Ph.D. preferred in Computational Biology, Bioinformatics, Biomedical Informatics, Computer Science, Statistics, Applied Mathematics or related field.
• Experience applying AI/ML in life-sciences settings with 2+ years post-PhD or 3–5 years industry experience.
• Track record in translational science, biomarker discovery or safety assessment.

Responsibilities

• Design, build, validate and deploy AI/ML solutions for translational safety prediction across discovery to real-world evidence.
• Develop predictive models and AI-reasoning frameworks for translational safety and outcomes.
• Integrate high-dimensional datasets to derive biological insights and de-risk safety signals.
• Collaborate with translational scientists and data teams to operationalize models and drive productization.
• Communicate complex methods clearly and maintain reproducible code and docs.

Skills

Advanced Analytics

Business Intelligence

Coaching

Collaboration

Critical Thinking

Data Analysis

Data Visualization

Statistical Modeling

Communication

Education

Ph.D. preferred in Computational Biology or related field

Job description

Johnson & Johnson is seeking a Senior Scientist – Data (AI) Scientist, Translational Safety, to accelerate drug safety prediction across discovery and development using advanced AI/ML analytics and multimodal data.

The role develops predictive models to identify translational biomarkers, support risk-minimization decisions, and connect discovery to real-world evidence. Collaboration with scientists across pharmacology, toxicology and data engineering is essential.