Computational Scientist, Spatial Omics

The Research Pathology Department, an integral part of Genentech's Research and Early Development Organization (gRED), is dedicated to ensuring that strategies for the treatment of diseases are grounded in accurate analyses of pathogenetic mechanisms. Building upon a strong foundation in digital pathology, the department is at the forefront of advancing spatial omics capabilities, integrating cutting-edge, tissue-based technologies with computational methods to enable high-resolution spatial profiling of biological systems. By collaborating with therapeutic areas and research scientists, the department supports the discovery, characterization, and development of treatments for a wide range of diseases.

DPIA-SO (Digital Pathology Image Analysis- Spatial Omics) is a specialized team within Research Pathology focused on collaborative spatial omics computational analysis. The team's mission is to provide scientists with actionable insights from high-dimensional imaging data by developing and employing transparent, reproducible, and scalable spatial analysis methods and pipelines. We are seeking a highly skilled and motivated Spatial Omics Computational Scientist to join our team.

Key Responsibilities

• Lead end-to-end analyses of single-cell spatial transcriptomics and proteomics datasets.

• Develop and apply advanced machine learning for spatial omics data analysis.

• Implement multi-modal data integration workflows for comprehensive biological insights.

• Analyze diverse spatial omics datasets from cutting-edge platforms (e.g., 10X Genomics Xenium/Visium HD, Lunaphore COMET).

• Collaborate with pathologists and researchers to interpret data and design experiments.

• Contribute to the advancement of spatial omics capabilities within Research Pathology.

• Advanced degree (M.S. with 5+ years relevant experience or Ph.D.) in Computational Biology, Bioinformatics, Biomedical Engineering, Data Science, Imaging Science or a related field.

• Demonstrated tissue-based image processing experience.

• Proficiency in Python programming.

• Experience with machine learning techniques and tools (e.g., TensorFlow, PyTorch and scikit-learn).

• Experience in statistical analysis, structured data analysis (e.g. high dimensional data reduction, validation and statistics) and data visualization.

• Excellent problem-solving, communication, and collaborative skills as well as communication skills to present findings and prepare reports.

• Expertise in single-cell spatial transcriptomics and/or spatial proteomics analysis.

• Hands-on experience with spatial omics platforms (e.g., 10X Genomics Xenium, Visium, Lunaphore COMET).

• Experience with multi-modal data integration combining various omics datasets.

• Basic knowledge of tissue histology and cell biology.

• Knowledge of computational and/or digital pathology workflows.

• Familiarity with tools like the scverse ecosystem (Scanpy, Squidpy, SpatialData) and image processing libraries (OpenCV, scikit-image).

• Understanding of infrastructure for managing large-scale imaging and single-cell data, including AWS cloud-based storage and distributed computing.

• Familiarity with tissue imaging techniques such as multiplexed immunofluorescence, in situ hybridization, or related methods.

• Background in experimental design for spatial omics or multi-modal studies.

Relocation benefits are available for this posting.

The expected salary range for this position based on the primary location of South San Francisco, CA is $109,500 - $203,300. Actual pay will be determined based on experience, qualifications, geographic location, and other job-related factors permitted by law. A discretionary annual bonus may be available based on individual and Company performance. This position also qualifies for the benefits detailed at the link provided below.

Benefits

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Genentech is an equal opportunity employer. It is our policy and practice to employ, promote, and otherwise treat any and all employees and applicants on the basis of merit, qualifications, and competence. The company's policy prohibits unlawful discrimination, including but not limited to, discrimination on the basis of Protected Veteran status, individuals with disabilities status, and consistent with all federal, state, or local laws.

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