Principal Scientist, Imaging Analytics

About Innovative Medicine

Our expertise in Innovative Medicine is informed and inspired by patients, whose insights fuel our science-based advancements. Visionaries like you work on teams that save lives by developing the medicines of tomorrow.

Join us in developing treatments, finding cures, and pioneering the path from lab to life while championing patients every step of the way.

Learn more at https://www.jnj.com/innovative-medicine

We are 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. In this role, you will transform routinely collected oncology trial scans (CT, MRI, PET) into earlier, evidence-based clinical-development decisions. You will advance foundational imaging-AI capabilities, develop novel quantitative imaging metrics, and deliver insights that enhance response assessment, dose optimization, and go/no-go decisions.

This is a high-impact, high-visibility role for a scientist who is passionate about translating cutting-edge imaging AI from research into real-world clinical impact. You will serve as a scientific bridge across data science, clinical development, and external partners.

This position can be based in New Brunswick/Raritan, NJ; Spring House/Horsham, PA; or Boston, MA.

Key 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 through hands-on research.
• 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.)

Required Knowledge, Skills and Abilities

• 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.