Principal Scientist, Imaging Analytics

Overview

At Johnson & Johnson, we believe health is everything. Our strength in healthcare innovation empowers us to build a world where complex diseases are prevented, treated, and cured, where treatments are smarter and less invasive, and solutions are personal. Through our expertise in Innovative Medicine and Med Tech, we are uniquely positioned to innovate across the full spectrum of healthcare solutions today to deliver the breakthroughs of tomorrow, and profoundly impact health for humanity.

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Guided by Our Credo, Johnson & Johnson is responsible to our employees who work with us worldwide. We provide an inclusive work environment where each person is considered an individual. We respect the diversity and dignity of our employees and recognize their merit.

Job Function: Data Analytics & Computational Sciences

Job Sub Function: Data Science

Job Category: Scientific/Technology

All Job Posting Locations: Cambridge, MA;
New Brunswick, NJ;
Raritan, NJ;
Spring House, PA

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

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. 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
.

• 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 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.).

• 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…