Society of Exploration Geophysicists
Computational Scientist
Society of Exploration Geophysicists, South San Francisco, California, us, 94083
BEPC has an open position for a Computational Scientist: Cancer Immunogenomics in South San Francisco, CA!!Contract W2 Period: 12-month contract with possibilities for extensionsPay & Benefits: $49-$80/hr. + Medical, Dental, Vision and Life InsuranceSchedule: Onsite or Hybrid in South San Francisco, CAOverview:
The Oncology Bioinformatics team in gRED Computational Sciences (gCS) is looking for an exceptional and motivated Computational Scientist (Contract) with a robust background in computational biology and/or cancer immunology. The individual will contribute to advancing personalized cancer immunotherapy research. Ideal candidates will have a proven record in bioinformatics, data science, and/or data engineering, with experience in MHC genomics, target, and neoantigen discovery and selection.Position Details:Full-time contract for an initial period of 1 year, with potential extension.Onsite or hybrid work arrangement within the Pacific time zone.Key Responsibilities:Develop and maintain bioinformatics and data infrastructure supporting individualized cancer immunotherapy research projects.Collaborate closely with computational biologists, AI scientists, and engineers to drive project success.Ensure high-quality documentation and data analysis practices for reproducibility.Present data and findings effectively to the Cancer Immunogenomics group and other collaborators.Requirements:Education : PhD or Master’s degree (with significant experience) in Computational Biology, Bioinformatics, or a related field.Technical Skills :Strong experience in bioinformatics and data infrastructure, including data curation, database management, and workflow development, particularly for AI/ML projects.Proficiency in programming languages such as R, Python, and shell scripting.Familiarity with version control systems (e.g., Git) and experience with high-performance computing environments.Preferred Experience :Background in complex data analysis or modeling related to MHC genomics or cancer immunology, particularly in cancer vaccine and cell therapy research.Experience with AI/ML techniques and/or statistical modeling.
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The Oncology Bioinformatics team in gRED Computational Sciences (gCS) is looking for an exceptional and motivated Computational Scientist (Contract) with a robust background in computational biology and/or cancer immunology. The individual will contribute to advancing personalized cancer immunotherapy research. Ideal candidates will have a proven record in bioinformatics, data science, and/or data engineering, with experience in MHC genomics, target, and neoantigen discovery and selection.Position Details:Full-time contract for an initial period of 1 year, with potential extension.Onsite or hybrid work arrangement within the Pacific time zone.Key Responsibilities:Develop and maintain bioinformatics and data infrastructure supporting individualized cancer immunotherapy research projects.Collaborate closely with computational biologists, AI scientists, and engineers to drive project success.Ensure high-quality documentation and data analysis practices for reproducibility.Present data and findings effectively to the Cancer Immunogenomics group and other collaborators.Requirements:Education : PhD or Master’s degree (with significant experience) in Computational Biology, Bioinformatics, or a related field.Technical Skills :Strong experience in bioinformatics and data infrastructure, including data curation, database management, and workflow development, particularly for AI/ML projects.Proficiency in programming languages such as R, Python, and shell scripting.Familiarity with version control systems (e.g., Git) and experience with high-performance computing environments.Preferred Experience :Background in complex data analysis or modeling related to MHC genomics or cancer immunology, particularly in cancer vaccine and cell therapy research.Experience with AI/ML techniques and/or statistical modeling.
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