Computational Pathology Scientist

Gilead Sciences•Foster City, CA

2d•$146,540 - $189,640•Onsite

About The Position

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.

Requirements

Bachelor's Degree and Six Years' Experience
Masters' Degree and Four Years' Experience

Nice To Haves

Advanced degree in a quantitative discipline (Computer Science, Biomedical Engineering, Physics, Mathematics, Statistics, or a related field)
Publication record in deep learning, machine learning, or statistics, particularly in digital pathology.
Strong understanding of medical image data formats and challenges associated with large pathology images (WSI, CODEX, ST).
Experience using Visiopharm or similar commercial image-analysis platforms for pathology image analysis.
Experience with brightfield IHC and multiplex IF image analysis.
Strong programming skills in Python and/or MATLAB applied to image processing and computer vision; experience with additional languages is a plus.
Proficiency with deep learning, data science, and image processing libraries such as PyTorch, Pandas, scikit-learn, NumPy, OpenSlide, OpenCV, MONAI, or Elastix.
Experience applying deep learning models to imaging data, including CNNs, segmentation and classification architectures, and transformer-based models; experience developing or adapting models for defined project needs is a plus.
Familiarity with classical machine learning algorithms such as Logistic Regression, Random Forest, and SVM.
Solid understanding of mathematical and statistical foundations of machine learning and medical image analysis, including optimization, registration, segmentation, classification, and predictive modeling.
Experience analyzing whole-slide images and large pathology imaging datasets from internal, public, or commercial sources.
Familiarity with cell biology and microscopy is a plus.
Experience leading end-to-end ML/DL/AI projects, including data engineering, model training and evaluation, compute and data resource planning, stakeholder communication, prioritization, and delivery of high-quality outcomes in fast-moving environments.
Up-to-date knowledge of advances in AI research and applications to medical imaging and digital pathology.
Excellent written and verbal scientific communication skills, with the ability to provide clear updates, summarize technical findings, and support effective decision-making across stakeholders.
Track record of delivering high-quality, well-documented work with attention to detail and appropriate quality control.
Ability to work independently while maintaining scientific rigor and effective collaboration across teams.

Responsibilities

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 stakeholders, while proactively aligning on priorities, timelines, and potential project risks.
Lead assigned projects from planning through execution, quality control, documentation, and delivery of well-organized outputs that support downstream use.