This role pays less than
82%
of similar roles. Most pay
$173,200–$256,212
— the shaded band above.
At the midpoint, this role pays about
$159k
versus about
$215k
for comparable roles.
Based on 240 similar postings.
Employer
About Johnson & Johnson
Johnson & Johnson is a multinational corporation operating in three main segments: consumer health products, pharmaceuticals, and medical devices, known for brands like Tylenol, Band-Aid, and Janssen. Industry: Pharmaceuticals & Medical Devices
Johnson & Johnson currently has
46 open roles
on FindRole.
Listed pay typically runs
$117,000–$201,250
across 42 roles with salary data.
Principal Scientist, Imaging Analytics joins the Interventional Oncology team to lead the scientific application of medical imaging Artificial Intelligence across an early-phase oncology portfolio. The role involves transforming routinely collected CT, MRI, and PET scans into evidence-based clinical-development decisions by developing novel quantitative metrics and advancing foundational AI capabilities like automated segmentation, radiomics, and multimodal predictive modeling. This position serves as a bridge between data science and clinical development, managing external partnerships with vendors and academic centers to accelerate model deployment. The ideal candidate possesses a PhD in a quantitative field and expertise in Python, PyTorch, MONAI, SimpleITK, and OpenCV. They will manage the full imaging data workflow, including DICOM I/O and harmonization, to solve complex problems regarding response assessment and dose optimization within oncology drug development programs.
Lead end-to-end AI applications on trial imaging data to develop quantitative measures and AI-derived endpoints.
Conduct hands-on research in automated segmentation, radiomics, and multimodal predictive modeling for oncology drug development.
Translate complex quantitative findings into clear scientific narratives for cross-functional stakeholders.
Provide scientific leadership to external partners including vendors, CROs, academic centers, and imaging OEMs.
Manage the full medical imaging data workflow including DICOM I/O, registration, harmonization, and segmentation of 3D images.
Publish and present scientific innovations at major clinical and technical conferences like MICCAI, AACR, and RSNA.
Develop evidence-based clinical decisions for oncology trials using CT, MRI, and PET scans.
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 like 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 medical imaging workflow including DICOM I/O, visualization, registration, harmonization, and segmentation of 3D images.
Strong peer-reviewed publication record and ability to communicate complex scientific concepts to technical and cross-functional audiences.
AWS
GraphRAG
Knowledge Graphs
Data Architecture
Data Engineering
Feature Stores
Metadata Management
SDTM
ADaM
MedDRA
FHIR
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DICOM
Data Governance
FAIR Data Principles
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Data Science
Artificial Intelligence
Generative AI
Agentic Workflows
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SQL
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Digital Health
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Imaging
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