Principal Scientist, Computational Image Analysis

Principal Scientist (Computational Image Analysis)

Responsibilities:
- Define quantitative imaging endpoints and biomarkers aligned to collaborators’ experimental goals
- Architect, develop, and validate deep learning-powered image processing pipelines for biomedical images across modalities (microscopy, histology, ultrasound, PET/CT, MRI)
- Execute image analysis workflows to generate quantitative results and create advanced visualizations to support interpretation
- Prepare and disseminate analytical deliverables; clearly communicate and document methods and findings
- Lead/drive data annotation and quality control to ensure research data integrity and reliability
- Present analytical plans, methods, and results in cross-functional meetings
- Contribute to data management efforts guided by FAIR principles to maintain data integrity and accelerate insights

Qualifications / Required:
- Advanced degree in Computer Science, Biomedical Engineering, Mathematics, or related field (PhD with 6+ years relevant experience)
- Ability to understand scientific questions in the context of drug development
- Proficiency in Python and strong computer science fundamentals
- Experience with version control and AI coding assistants (e.g., Claude Code)
- Strong understanding of modern image processing principles, including deep learning approaches

Skills / Preferred:
- Expertise in quantitative math for image analysis (e.g., optimization, regression, statistics, signal processing, cluster analysis, machine learning)
- Experience building custom pipelines in enterprise AI platforms (e.g., Dataiku)
- Knowledge of distributed computing frameworks (e.g., PySpark)
- Familiarity with biomedical image analysis tools (e.g., HALO, Imaris, 3D Slicer)
- Familiarity with data annotation workflows (e.g., v7 Darwin), data management platforms (e.g., PathCore, HALO Link, Flywheel), and relevant APIs/CLIs

Application instructions:
- Apply now.