Computational Pathology Scientist

At Gilead, we’re creating a healthier world for all people. For more than 35 years, we’ve tackled diseases such as HIV, viral hepatitis, COVID-19 and cancer – working relentlessly to develop therapies that help improve lives and to ensure access to these therapies across the globe. We continue to fight against the world’s biggest health challenges, and our mission requires collaboration, determination and a relentless drive to make a difference.

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Gilead is seeking an Imaging Data Scientist to apply AI, machine learning, and image analysis to digital pathology data supporting drug discovery and development. The role requires strong Python, computer vision, and deep learning skills, along with experience managing computational projects from planning through delivery.

Gilead Sciences is seeking a highly motivated imaging data scientist to join the computational pathology team within the Research Pathobiology group in Foster City, CA. The successful candidate will primarily support project-facing work across Gilead’s discovery and development pipeline by applying image analysis, deep learning, and machine learning approaches to advance understanding of pathobiology in oncology, virology, fibrosis, and inflammation. This role partners closely with scientific, clinical imaging, data management, and IT teams to deliver fit-for-purpose computational pathology analyses and scalable solutions that address defined program needs.

This role is primarily responsible for applying established and fit-for-purpose AI-based image analysis workflows to project‑facing questions in digital pathology, including extraction of histopathological endpoints, spatial analysis of tissue‑based imaging data, and high‑throughput workflow customization. The position supports imaging biomarker work across discovery and clinical drug development using pathology imaging data such as H&E, IHC, CISH, mIF, CODEX, and spatial transcriptomics.

Targeted method or workflow development may be undertaken when needed to address a defined pipeline need, improve scalability, or enable reliable delivery; however, the role’s primary emphasis is timely execution and support of project priorities. The role uses commercial, internal, and open‑source tools and requires cross‑functional collaboration with scientific, technical, and data stakeholders. Success depends on strong end‑to‑end ownership, early escalation of risks or blockers, and clear translation of scientific questions into actionable analytic plans.

• Apply, evaluate, and validate computational pathology approaches, advanced analytics, and computer vision tools to support project-specific histopathological endpoints, imaging biomarkers, and spatial analyses across Gilead’s discovery and development pipelines.
• Collaborate with cross-functional scientific colleagues to design and execute analytic strategies for tissue-based endpoints and imaging biomarkers.
• Curate and prepare large imaging datasets for project analyses and, when justified by a defined pipeline need, for training or adapting targeted deep‑learning and pathology foundation models.
• Contribute to imaging data management and targeted workflow improvements that increase the reliability, efficiency, or scalability of project-facing computational pathology analyses.
• Communicate findings, progress, and recommendations clearly through reports, presentations, and publications for expert and non-expert…