AI Scientist - Image Analysis & Digital Pathology

AI/ML Scientist - Oncology Data Science Team

Location: Cambridge or Basel Onsite Relocation is offered for this role. 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 deep expertise in digital pathology and a strong curiosity for translational research to join the AI & Innovation Team. This role will apply advanced AI approaches to generate insights from complex imaging datasets and support our oncology drug discovery programs.

Key Responsibilities

• Develop and apply AI / deep learning approaches to extract insights from digital pathology imaging datasets (e.g., H&E, IHC, mIF, Spatial transcriptomics).
• Work with foundational models, including pre-trained, self-supervised, multi-purpose, and multi-modal models to advance generative AI applications in drug discovery.
• Support imaging biomarker development efforts by analyzing pathology imaging data and collaborate with pathologists and translational teams to interpret image-derived biomarkers and biological findings.
• Contribute to evaluation, validation, and benchmarking of image analysis algorithms and workflows.
• Perform tissue segmentation, cell phenotyping, feature extraction, and spatial analysis using state-of-the-art computational approaches.
• Stay current with advances in computational pathology, AI/ML, and spatial biology technologies; contribute to scientific publications; and present results at internal and external scientific conferences.

Requirements

• Ph.D. in Machine Learning, Computer Science, Applied Mathematics, Computational Biology or related field. Candidates with MSc in Machine Learning, Computer Science, Applied Mathematics, Computational Biology or related field, with 4+ years of relevant industry experience will also be considered.
• Strong experience analyzing pathology imaging modalities including H&E, IHC, multiplex IF, and / or spatial transcriptomics data.
• Understanding of machine learning methods for segmentation, classification, detection, and representation learning.
• Experience in one or more of the following areas: generative AI, digital pathology foundation models, geometric deep learning and / or multi-modal learning.
• Excellent programming skills and proficiency in deep learning frameworks such as PyTorch, with openness to learning new tools and technologies.
• Strong communication and collaboration skills with the ability to communicate complex data insights and recommendations to cross-functional teams.
• Strong research skills, evidenced by publications in leading scientific journals and / or conference presentations.

The rewards of being part of our team go far beyond base pay and incentives. We also offer a variety of competitive benefits in kind to help you thrive personally and professionally, such as insurance plans, retirement plans, wellbeing resources and global recognition programs. In addition, we provide flexible and hybrid working options, where possible, and a minimum of 14 weeks paid parental leave.

Expected Annual Base Salary Range for role: US: 126,000.00 - 234,000.00 USD Annual. The salary offered is determined based on gender-neutral objectives, such as relevant skills, competencies and experience in accordance with the Novartis pay setting policy and upon joining Novartis will be reviewed periodically.

In addition to your base salary, you may be eligible for a performance-based bonus depending on certain performance parameters. Further details will be provided during the application process.

Pay equity is a fundamental principle of our employment policy and reflects our commitment to create a diverse, equitable and inclusive environment that treats all employees with dignity and respect, as outlined in our Code of Ethics.

The Novartis Group of Companies are Equal Opportunity Employers. We do not discriminate in recruitment, hiring, training, promotion or other employment practices for reasons of race, color, religion, sex, national origin, age, sexual orientation, gender identity or expression, marital or veteran status, disability, or any other legally protected status.