Director, AI for Biomedical Discovery

U175 (FCRS = US175) Novartis Institutes for BioMedical Research, Inc.; San Diego (CA); On-site; USD 195,000 - 361,000; Full time

Job summary

Novartis Institutes for BioMedical Research, DARe in San Diego, seeks a Director of Data Science & AI to shape our global strategy, lead the US data science capability, and connect computational, experimental, and translational teams to accelerate target discovery.

You will build and mentor a top-tier US team, define a portfolio of AI initiatives, and translate scientific questions into computational plans using foundation models, multimodal learning, and data integration across omics and

Qualifications

• PhD in a quantitative discipline with biology understanding.
• 10+ years applying AI to biomedical research or drug discovery.
• Proven success leading multidisciplinary global teams.

Responsibilities

• Lead US DARe Data Science & AI efforts and shape global AI strategy.
• Define prioritized AI initiatives with measurable impact on discovery and design.
• Develop and apply foundation models, multimodal learning, and biomedical knowledge graphs.
• Build and grow a high-performing US team spanning AI, computational biology, and data science.
• Establish external partnerships to accelerate AI capabilities and translation to targets.

Skills

AI strategy

Python & DL frameworks

Cross-functional leadership

Multimodal learning

Data governance

Communication

Stakeholder management

Education

PhD in Machine Learning or related quantitative field

Tools

Python

Deep learning frameworks (PyTorch/TensorFlow)

Git

Job description

Novartis Institutes for BioMedical Research, DARe in San Diego, seeks a Director of Data Science & AI to shape our global strategy, lead the US data science capability, and connect computational, experimental, and translational teams to accelerate target discovery.

You will build and mentor a top-tier US team, define a portfolio of AI initiatives, and translate scientific questions into computational plans using foundation models, multimodal learning, and data integration across omics and