Data Scientist, AI for Biomedical Imaging

What you'll do

• Lead AI-enabled image analysis strategies for complex biological imaging workflows in partnership with wet-lab scientists.
• Develop, validate, and deploy robust algorithms to characterize cellular, organoid, and tissue phenotypes.
• Drive the adoption of deep learning, vision foundation models, and representation learning for high-dimensional datasets.
• Define rigorous benchmarking and evaluation strategies to guide method selection and ensure scientific value.
• Create scalable, reusable image analysis workflows in collaboration with data engineering and platform teams.
• Translate state-of-the-art AI methods into practical, validated workflows for drug discovery projects.
• Communicate complex AI and image analysis concepts to experimental scientists and non-technical stakeholders.

What we're looking for

• PhD in computer science, AI, machine learning, biomedical image analysis, computational imaging, data science, or a related quantitative field.
• 3+ years of applied experience in AI for bioimaging and computer vision.
• Experience developing and validating image analysis algorithms for biological, biomedical, or pharmaceutical research applications.
• Proficiency in Python and the scientific deep learning stack including PyTorch, Hugging Face, Lightning, and MONAI.
• Experience with image analysis tools such as scikit-image, OpenCV, napari, Cellpose, StarDist, InstanSeg, and OME-Zarr.
• Ability to work in Linux-based high-performance computing, cloud, or large-scale data processing environments.
• Experience developing foundation models, self-supervised learning, or multimodal AI models for biological imaging (preferred).
• Familiarity with the drug discovery pipeline, phenotypic screening, or pharmaceutical research processes (preferred).