AI Scientist – AI-Driven Target Identification

Novartis•Cambridge, MA

11d•$103,600 - $192,400•Onsite

About The Position

The Oncology Data Science team in Biomedical Research at Novartis works at the intersection of oncology drug discovery, computational biology, AI/ML, and data engineering. We are seeking an enthusiastic AI/ML scientist with strong curiosity for AI-driven drug discovery to join the AI & Innovation team. This role will apply advanced AI approaches to generate insights from complex multi-modal datasets and advance our target and biomarker discovery efforts.

Requirements

Ph.D. in Machine Learning, Computer Science, Applied Mathematics, Computational Biology or related field.
Strong experience in one or more of the following areas: generative AI, biomedical foundation models, geometric deep learning, multi-modal learning, and large-scale knowledge graphs.
Excellent programming skills and proficiency in deep learning frameworks such as PyTorch, with openness to learning new tools and technologies.
Practical experience across ML and LLM software stack, including feature engineering, model development, deployment, and validation.
Prior experience working with omics data and familiarity with oncology drug development.
Excellent communication skills, with the ability to communicate complex data insights and recommendations to cross-functional teams and stakeholders.
Demonstrated strong research skills, evidenced by publications in top-tier machine learning or artificial intelligence conferences and/or leading scientific journals.

Responsibilities

Design, develop, and apply advanced AI/ML approaches to extract actionable insights from pre-clinical, clinical and real-world evidence datasets.
Demonstrate value of innovative AI techniques in the context of drug target identification, biomolecular interaction modeling, drug development and biomarker discovery.
Work with foundational models, including pre-trained, self-supervised, multi-purpose, and multi-modal models to advance generative AI applications in drug discovery.
Collaborate with cross-functional teams to develop and adopt best practices for ML-ready data.
Contribute to scientific publications and present results at internal and external scientific conferences.