Principal Scientist, Imaging Analytics

About the role

Johnson & Johnson is seeking a Principal Scientist, Imaging Analytics to join Interventional Oncology (INTO) and lead the scientific application of medical imaging Artificial Intelligence across our early-phase oncology portfolio.

Responsibilities

• Lead end-to-end AI applications on trial imaging data (CT, MRI, PET) for quantitative imaging measures and AI-derived endpoints.
• Collaborate internally and externally to drive scientific innovation in foundational imaging AI that are relevant to oncology drug development — including automated segmentation, radiomics, and multimodal predictive modeling.
• Translate imaging-derived evidence into actionable insights by converting complex quantitative findings into clear scientific narratives and engaging cross-functional stakeholders.
• Provide scientific leadership to external partnerships — including imaging AI vendors, CROs, biomarker companies, academic centers, and imaging OEMs — to accelerate model development, validation, and deployment.
• Publish and present scientific innovation at top scientific and clinical conferences (e.g., MICCAI, AACR, RSNA, etc.).

Requirements

• 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 (CT, MRI, PET) in a clinical setting.
• Hands-on expertise across the medical imaging AI stack: deep learning (segmentation, detection, classification, registration), radiomics, and multimodal predictive modeling.
• Proficiency in Python and PyTorch, with practical experience in medical-imaging 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: 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 both technical and cross-functional audiences.

Preferred Experience

• In analyzing solid-tumor imaging, particularly lung and head & neck (H&N).
• Track record of developing and applying AI/ML to oncology imaging and within oncology clinical trials.
• Familiarity with standard oncology endpoints (e.g., RECIST 1.1, iRECIST, PERCIST).
• Experience building and scaling clinical or imaging AI platforms end-to-end, including data ingestion, harmonization, model inference, visualization, and continuous monitoring.
• Experience in sourcing, structuring, and managing external partnerships with imaging-AI vendors, CROs / imaging core labs, biomarker companies, and academic centers.