Imaging Data Scientist, Digital Pathology & AI

Gilead Sciences Inc.

Maryland

On-site

USD 147,000 - 190,000

Full time

14 days+

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Benefits offered by this job

Annual bonus
Stock-based long-term incentives
Paid time off
Medical, dental, vision insurance

Job summary

Gilead Sciences Inc. 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 Python, computer vision, and deep learning skills, with experience managing computational projects from planning through delivery. The position focuses on applying AI-based image analyses to histopathology endpoints, spatial tissue analyses, and high-throughput workflows, collaborating across scientific,

Qualifications

  • Bachelor's or master's with multiple years of imaging data experience.
  • Strong Python, ML/DL, and computer vision fundamentals.
  • Experience with digital pathology data formats and large WSIs.

Responsibilities

  • Apply and validate computational pathology approaches for imaging biomarkers.
  • Collaborate to design analytic strategies for tissue endpoints.
  • Prepare large imaging datasets for analyses and model training.
  • Communicate findings to stakeholders through reports and presentations.
  • Lead projects from planning to delivery with quality control.

Skills

Python
Computer vision
Deep learning
Image processing
Pytorch
OpenCV
MATLAB
Pandas

Education

Advanced degree in quantitative field
PhD or related publication record

Tools

OpenSlide
MONAI
Elastix
CODEX

Job description

Gilead Sciences Inc. 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 Python, computer vision, and deep learning skills, with experience managing computational projects from planning through delivery. The position focuses on applying AI-based image analyses to histopathology endpoints, spatial tissue analyses, and high-throughput workflows, collaborating across scientific,

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