Director of AI & ML for Biomedical Discovery

Novartis; San Diego (CA); On-site; USD 250,000 - 350,000; Full time

Job summary

Novartis DARe is seeking a visionary Director of Data Science & AI to shape the company’s global AI strategy and to build a robust US capability in San Diego. You will connect computational and experimental teams, accelerate target discovery, and drive translational science through advanced AI programs.

The role requires leading a multidisciplinary team, delivering high-impact AI projects, and partnering across departments to translate biology questions into computational plans that advance drug

Qualifications

• PhD in a quantitative field with biology understanding.
• Experience applying AI to biomedical research or drug discovery.
• Proven leadership of multidisciplinary global teams across AI and biology.

Responsibilities

• Lead US Data Science & AI efforts and shape global strategy.
• Define a portfolio of AI initiatives with measurable impact.
• Guide AI programs across target identification and validation.
• Develop biological foundation models and multimodal learning approaches.
• Collaborate with genetics, omics, imaging, and EHR data teams.
• Foster growth for early-career talent in the US team.
• Evaluate external partnerships to accelerate capabilities.
• Translate scientific questions into computational strategies.

Skills

Python

Deep learning frameworks

Version control

Leadership

Communication

Education

PhD in Machine Learning, Computational Biology, Data Science, Computer Science, or related quantitative discipline

Tools

Git

TensorFlow/PyTorch

Job description

Novartis DARe is seeking a visionary Director of Data Science & AI to shape the company’s global AI strategy and to build a robust US capability in San Diego. You will connect computational and experimental teams, accelerate target discovery, and drive translational science through advanced AI programs.

The role requires leading a multidisciplinary team, delivering high-impact AI projects, and partnering across departments to translate biology questions into computational plans that advance drug