Senior Scientist, Imaging Analytics

What you'll do

• Develop imaging AI and analytics to derive quantitative measures and endpoints from CT, MRI, and PET trial data.
• Conduct hands-on research on automated segmentation, radiomics, and multimodal predictive modeling for oncology drug development.
• Provide scientific leadership to external partners including AI vendors, CROs, and academic centers to accelerate model deployment.
• Transform oncology trial scans into evidence-based clinical decisions regarding response assessment and dose optimization.
• Publish and present scientific innovations at top clinical and scientific conferences such as MICCAI and RSNA.
• Manage the end-to-end imaging data workflow including DICOM I/O, registration, harmonization, and 3D image segmentation.
• Build and scale clinical AI platforms for data ingestion, model inference, and continuous monitoring.

What we're looking for

• Ph.D. in Computer Science, Biomedical Engineering, Electrical Engineering, or a related quantitative field.
• Hands-on expertise in medical imaging AI including deep learning, radiomics, and multimodal predictive modeling.
• Proficiency in Python and PyTorch with experience in libraries such as MONAI, SimpleITK, and nnU-Net.
• Experience with cloud ML infrastructure and MLOps practices for scalable training and inference.
• Extensive experience with the full imaging data workflow including DICOM I/O, registration, and 3D image segmentation.
• Strong peer-reviewed publication record and ability to communicate complex scientific concepts to technical and cross-functional audiences.
• Post-doctoral or industry experience developing AI/ML for medical imaging in a clinical setting (preferred).
• Experience in analyzing solid-tumor imaging and familiarity with standard oncology endpoints (preferred).