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Position: Director, Research Data Sciences
Institution: Gilead Sciences, Inc.
Location: Foster City, California, United States
Duties: Provide deep expertise in statistical and ML methods, and applications to accelerate research discovery; Establish and drive external collaborations in cutting-edge analytical method development and technology platforms; Collaborate with cross-functional teams to identify, evaluate, and deliver solutions to support Gilead Research objectives; Hands-on analysis of internal and external datasets and development of AI/ML models to support decision making and scientific insights; Collaborate cross therapeutic areas to deliver impact on research programs, examples including but not limited to statistical modeling of TCR repertoires, deep learning for peptide-MHC-binding and antigen epitopes predictions, etc
Requirements: PhD in computational biology, computer science, data science, physical science, statistics, or a related field with a minimum of 10+ years of relevant work experience. Industry experience and people management experience is preferred; Demonstrated track record and expertise in AI/ML method development and applications, e.g. deep neural networks, active learning, Bayesian optimization, LLM, etc; Excellent interpersonal and communication skills that foster collaboration and teamwork; Proficiency in R, Python, or C/C++ programming languages, shell scripting and command line tools
   
Text: Director, Research Data Sciences Provide deep expertise in statistical and ML methods, and applications to accelerate research discovery; Establish and drive external collaborations in cutting-edge analytical method development and technology platforms; Collaborate with cross-functional teams to identify, evaluate, and deliver solutions to support Gilead Research objectives; Hands-on analysis of internal and external datasets and development of AI/ML models to support decision making and scientific insights; Collaborate cross therapeutic areas to deliver impact on research programs, examples including but not limited to statistical modeling of TCR repertoires, deep learning for peptide-MHC-binding and antigen epitopes predictions, etc PhD in computational biology, computer science, data science, physical science, statistics, or a related field with a minimum of 10+ years of relevant work experience. Industry experience and people management experience is preferred; Demonstrated track record and expertise in AI/ML method development and applications, e.g. deep neural networks, active learning, Bayesian optimization, LLM, etc; Excellent interpersonal and communication skills that foster collaboration and teamwork; Proficiency in R, Python, or C/C++ programming languages, shell scripting and command line tools
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