Principal Scientist, Imaging Analytics

What you'll do

• Lead end-to-end AI applications on trial imaging data to develop quantitative imaging measures and AI-derived endpoints.
• Conduct hands-on research in automated segmentation, radiomics, and multimodal predictive modeling for oncology drug development.
• Convert complex quantitative imaging findings into clear scientific narratives for cross-functional stakeholders.
• Provide scientific leadership to external partners, including AI vendors, CROs, and academic centers, to accelerate model deployment.
• Translate imaging-derived evidence into actionable insights for response assessment and dose optimization.
• Publish and present scientific innovations at top-tier scientific and clinical conferences.
• Manage the full imaging data workflow, including DICOM I/O, registration, harmonization, and 3D image segmentation.

What we're looking for

• Ph.D. in Computer Science, Biomedical Engineering, Electrical Engineering, or a related quantitative field.
• 3+ years of post-doctoral or industry experience developing AI/ML for medical imaging in a clinical setting.
• Hands-on expertise in deep learning (segmentation, detection, classification, registration), radiomics, and multimodal predictive modeling.
• Proficiency in Python and PyTorch with experience in libraries such as MONAI, SimpleITK, ITK, PyRadiomics, nnU-Net, 3D Slicer, and OpenCV.
• Experience with cloud ML infrastructure and MLOps practices for scalable training and inference on imaging data.
• Extensive experience with the full imaging data workflow including DICOM I/O, visualization, registration, harmonization, annotation, and segmentation of 3D medical images.
• Strong peer-reviewed publication record and demonstrated ability to communicate complex scientific concepts to technical and cross-functional audiences.
• Experience in analyzing solid-tumor imaging, particularly lung and head & neck (preferred).