Senior Principal Scientist / Assoc Director, Oncology Translational Research
$138,600–$257,400 year
On-site · Cambridge, Massachusetts, United States
Job Summary
Imaging Scientist sought to lead AI-enabled image analysis in Oncology Translational Research. Develop scalable workflows for pathology whole-slide image acquisition, processing, analysis, and interpretation; apply AI/ML and quantitative image analysis to biomarker questions across oncology programs; serve as SME for HALO/HALO AI workflows and harmonize across Cambridge and Basel; build robust, reproducible analytical processes; develop pipelines for high-plex imaging data (RareCyte Orion) and integrate outputs with biomarker datasets; collaborate with cross-functional teams to inform project decisions and drive translational science in oncology.
Required Qualifications
- 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: background in cancer biology, immuno-oncology, radioligand therapy, spatial biology, or tumor microenvironment biology
- Proficiency in Python or R
- Experience harmonizing digital pathology workflows across sites, teams, or software environments
- FAIR principles or enterprise data platforms
- Publications or collaborations in digital pathology, oncology biomarkers, image analysis, or spatial biology
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