Data Scientist, AI for Biomedical Imaging

Overview

In this role you apply AI-enabled image analysis to advanced biological imaging, partnering with wet-lab scientists to turn complex questions into robust, reproducible analysis workflows. You lead method development in segmentation, representation learning, and foundation models, while ensuring scalable deployment and cross-disciplinary collaboration. You will serve as the imaging-AI scientific lead within Discovery Sciences, shaping projects and enabling data-driven drug discovery decisions. This is a hands-on, collaborative position with a strong emphasis on scientific impact and reproducibility.

Pay / Benefits

• Health, life and disability benefits
• 401(k) with company contribution and match
• Performance-based cash incentive
• Eligibility for equity awards
• Generous time off package

Responsibilities

• Lead AI-driven image analysis strategies for complex imaging workflows alongside wet-lab scientists
• Define benchmarking and evaluation plans to compare methods and guide model selection
• Develop, validate, and deploy image analysis algorithms for cellular, organoid, tissue phenotypes in high-throughput imaging
• Advance AI methods for imaging (deep learning, foundation models, embedding-based phenotyping, multimodal integration) into practical workflows
• Contribute to scalable, reusable image analysis workflows with data science, data engineering, and platform teams
• Promote best practices across the workflow lifecycle and ensure reproducibility, version control, and provenance

Key requirements

• PhD with 3+ years of applied AI for bioimaging or computer vision
• Experience developing and validating image analysis algorithms for biological or pharmaceutical applications
• Proven ability to design benchmarking/evaluation strategies for model selection
• Proficiency with Linux-based HPC/cloud environments and reproducible research practices
• Strong Python skills and deep learning stack (PyTorch, Hugging Face, Lightning, MONAI)
• Hands-on experience with image analysis tools (scikit-image, OpenCV, napari, Cellpose, StarDist, InstanSeg, OME-Zarr)
• Ability to communicate complex AI concepts to diverse audiences
• Collaborative, multidisciplinary mindset and scientific judgement

• Excellent scientific communication
• Stakeholder engagement
• Curiosity and learning agility
• Python
• PyTorch
• Hugging Face

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