Scientist, Digital Pathology

We are seeking an innovative and highly motivated image analysis scientist to join the Pathobiology group in support of discovery research and development projects throughout Gilead Sciences pipeline.

The successful candidate will work closely with internal and external stakeholders to identify and validate novel drug targets and further our understanding of the pathobiology in areas of oncology, fibrosis, virology, and inflammation.

Scope of responsibilities include research and development of AI based image analysis pipelines, customizing and executing high-throughput workflows and generating decisive data presentations. You will have a background in computer science, physics or related quantitative discipline. Experience with open source image analysis and deep-learning software packages (e.g. Matlab, Python, Cuda) as well as knowledge of image analysis techniques (e.g. filtering signal channels, object segmentation, feature detection and classification, spatial analysis) is required.

The successful candidate must be self-motivated and detail-oriented, exhibit excellent organizational and communication skills, demonstrate the ability to manage multiple priorities and work under tight timelines, thrive with a high degree of responsibility, is a proficient team-player in a cross-functional and highly collaborative team environment. Applicants should be able to troubleshoot independently and show curiosity-driven engagement in scientific and computational problems

Essential Duties and Job Functions:

• Evaluate and implement new computational approaches in digital pathology to extract histopathological endpoints and conduct spatial analyses
• Collaborate with cross-functional scientific colleagues to design and execute meaningful analytic strategies for tissue-based endpoints.
• Communicate effectively through reports, presentations and publications with non-expert scientific stakeholders.
• Identify best practices and innovation opportunities relevant to image analysis projects.

Knowledge, Experience, and Skills:

• Graduate degree (MSc or PhD) in computer science/engineering, physics, mathematics or related discipline. Postdoctoral experience is a plus.
• Programming skills and experience with one or more scripting languages (e.g. Matlab, Python, Cuda) is essential
• Experience in designing convolutional neural networks and their applications to image processing and computer vision
• Familiarity with the AWS cloud is a plus
• Analysis experience using whole-slide images is preferred.
• Expertise in predictive modeling using statistical and machine learning methods
• Experience in manipulating, analyzing and visualizing large internal, public and commercial datasets and is a plus
• Familiarity with cell biology and microscopy is a plus