Computational Pathology Scientist

Role: Imaging Data Scientist supporting digital pathology within Gilead's Research Pathobiology group, applying AI, machine learning, and image analysis to enhance drug discovery and development across oncology, virology, fibrosis, and inflammation. Responsibilities: develop, evaluate, and validate computational pathology workflows for histopathological endpoints, spatial tissue analysis, and biomarker extraction using diverse pathology imaging data (H&E, IHC, mIF, CODEX, spatial transcriptomics); collaborate cross-functionally to design analysis strategies; curate large imaging datasets; contribute to workflow improvements; communicate findings through reports and presentations; lead project execution from planning to delivery. Qualifications: Bachelor's degree with 6+ years or Master's with 4+ years in a quantitative discipline (e.g., Computer Science, Biomedical Engineering), with expertise in Python, deep learning (PyTorch, MONAI), computer vision, and medical image data formats; experience with pathology image analysis platforms, large datasets, and applying ML models (CNNs, transformers); strong statistical and mathematical grounding; excellent communication skills. Preferred: publication record in digital pathology, experience with multiplex imaging, cell biology knowledge, and project leadership in AI for medical imaging. High-Value: focus on training/adapting AI models for tissue-based endpoints, working with high-throughput pathology data, supporting discovery and clinical phases, and cross-functional stakeholder partnership. Location: Foster City, CA, primarily remote/hybrid with collaboration expectations.