Location
Cambridge (USA) #Onsite
The Oncology Pathology and Biomarkers group within Oncology Translational Research at Novartis Biomedical Research is seeking an accomplished Imaging Scientist to join the Image Analysis team in Cambridge.
This role is for a scientific and technical leader with expertise in digital pathology, computational image analysis, AI/ML, and tissue-based oncology biomarkers. The successful candidate will develop and support scalable workflows for whole-slide pathology and multiplexed tissue imaging data.
The position will expand OPB’s AI-enabled image analysis capabilities and deliver scalable solutions for digital pathology and translational biomarker assessment across oncology programs.
The scientist will support spatial analysis of tumor samples, help establish pipelines for RareCyte Orion high-plex imaging data, and strengthen harmonized HALO AI workflows across Cambridge and Basel.
Key Responsibilities
Digital Pathology and Imaging Strategy
- Develop and deploy scalable workflows for pathology whole-slide image acquisition, processing, analysis, and interpretation
- Apply AI/ML and quantitative image analysis to address biological, translational, and biomarker questions in human and preclinical samples
- Provide expertise in digital pathology, spatial analytics, and AI-enabled biomarker quantification across oncology programs
- Evaluate emerging computational imaging approaches that strengthen OPB biomarker capabilities and decisional data delivery
HALO AI Support and Cross-Site Harmonization
- Serve as a subject matter expert for HALO and HALO AI workflows and drive harmonization across Cambridge and Basel
- Build robust, reproducible, and well-documented analytical processes that improve operational resilience and cross-site consistency
- Improve image data organization, sharing, and best practices to support collaboration with Oncology Data Science, Data42, and enterprise AI initiatives
AI/ML-Enabled Image Analysis
- Build, optimize, and apply AI/ML models and reproducible workflows for whole-slide, multiplexed, and high-plex tissue imaging
- Use tools such as HALO and HALO AI for segmentation, classification, feature extraction, spatial analysis, and biomarker scoring
- Translate image-derived outputs into clear biological insights that inform project decisions
High-Plex Imaging and RareCyte Orion Analysis
- Develop and implement pipelines to process, analyze, visualize, and interpret RareCyte Orion and other high-plex imaging data
- Partner with OPB scientists to define fit-for-purpose analytical approaches for multiplexed tissue imaging studies
- Integrate high-plex imaging outputs with broader biomarker and translational datasets
- Establish quality control, documentation, and reporting standards that support innovative use of multiplexed imaging in oncology
Cross-Functional Collaboration and Impact
- Partner with scientists, pathologists, physician-scientists, computational biologists, data scientists, and project teams to design, analyze, and interpret tissue-based studies
- Engage across Oncology Translational Research and broader Novartis Biomedical Research to identify stakeholder needs, address workflow gaps, and implement practical solutions
- Communicate image analysis strategies, recommendations, and findings clearly to multidisciplinary teams while contributing to a collaborative culture of scientific excellence and innovation
Essential requirements
- This is a dual posting. The final level of the offered role will be determined by the hiring team based on the skills, experience, and capabilities required to perform the role at the offered level.
- PhD or MS in biology, bioinformatics, biomedical engineering, computational biology, data science, pathology, or a related field
- Minimum 5 years of industry experience
- Significant experience in digital pathology, computational image analysis, imaging data science, translational oncology, or tissue-based biomarker research
- Expertise with image analysis tools such as HALO and experience developing, implementing, and applying AI/ML models to tissue images
- Demonstrated success applying advanced imaging solutions to pathology and translational research workflows, including high-resolution whole-slide and gigapixel image datasets
- Strong understanding of tissue-based biomarker development, oncology biology, and translational research
- Strong organizational, communication, and problem-solving skills, with the ability to engage stakeholders, identify process gaps, and drive next steps
- Ability to work effectively in multidisciplinary, matrixed teams and contribute in a collaborative, innovative, and self-directed way
Desirable Qualifications
- Background in cancer biology, immuno-oncology, radioligand therapy, spatial biology, or tumor microenvironment biology
- Experience supporting oncology drug discovery or early clinical development with image-based biomarker strategies and spatial scoring approaches for pharmacodynamic, indication, isotope, or patient selection decisions
- Familiarity with RareCyte Orion or other multiplexed/high-plex tissue imaging platforms, including machine learning and computational methods for tissue image analysis
- Proficiency in Python or R
- Experience harmonizing digital pathology workflows across sites, teams, or software environments, with familiarity in image data centralization, digital pathology infrastructure, FAIR principles, or enterprise data platforms
- Experience collaborating with oncology data science, informatics, translational medicine, or enterprise AI teams
- Scientific visibility through publications, presentations, or collaborations in digital pathology, oncology biomarkers, image analysis, or spatial biology
