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Hong Lab - Data Science Research Specialist

Recruitment Period

Open date: November 26th, 2019

Last review date: Wednesday, Dec 11, 2019 at 11:59pm (Pacific Time)
Applications received after this date will be reviewed by the search committee if the position has not yet been filled.

Final date: Wednesday, May 26, 2021 at 11:59pm (Pacific Time)
Applications will continue to be accepted until this date, but those received after the review date will only be considered if the position has not yet been filled.

Description

A data science research specialist opportunity is available at the University of California, San Francisco in the Hong laboratory (honglab.ucsf.edu). The Hong lab is part of both the Department of Radiation Oncology and the Bakar Computational Health Sciences Institute.

The Hong lab focuses on combining clinical domain knowledge with data science to generate insights from real world data, develop actionable computational tools, and evaluate the benefit of these advances for personalized cancer care. We have a specific interest and expertise in machine learning, natural language processing, computational data extraction, and imaging analytics. We apply these methods to identify new knowledge regarding clinical practice and patient outcomes, make actionable predictions, and identify new interventions. Our lab works from end-to-end along the development and implementation pipeline to develop tools for clinicians to make a meaningful difference in patient care.

This research specialist will perform informatics research to develop computational tools and analyses related to personalized oncology care. Specialist responsibilities include data management, implementation and programming of computational tools including machine learning/deep learning algorithms, and collaboration with other members of the lab and affiliated groups. They will work closely with other lab members, participate in lab meetings, and perform other duties as assigned. They will be expected to conduct data analyses and prepare for presentations at lab meeting and journal club. The specialist will join a multidisciplinary team of clinicians and scientists in the Department of Radiation Oncology and the Bakar Computational Health Sciences Institute.

The specialist will also be encouraged to take advantage of other training and learning opportunities within the UCSF Helen Diller Family Comprehensive Cancer Center, the Bakar Computational Health Sciences Institute, and other related entities at UCSF. We hope this experience will prepare the applicant for future opportunities in medicine and informatics.

Required Qualifications:

  • B.A. or B.S. (or equivalent) in bioinformatics, computer science, data science, computational biology, mathematics, or a related quantitative/scientific discipline (by time of hire)
  • Strong programming skills, with a preference for experience in Python, R, SQL
  • Strong problem-solving skills
  • Strong communication and writing skills

Preferred Qualifications:

  • Experience with data science, machine learning, and/or natural language processing
  • Experience developing experimental and analytical plans
  • Prior research experience

Applicant’s materials must list (pending) qualifications upon submission.

UC San Francisco seeks candidates whose experience, teaching, research, or community service has prepared them to contribute to our commitment to diversity and excellence.

The University of California is an Equal Opportunity/Affirmative Action Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, age or protected veteran status.

Please apply online at https://aprecruit.ucsf.edu/JPF02765

Job location

San Francisco

Requirements

Document requirements
  • Curriculum Vitae - Your most recently updated C.V.

  • Cover Letter (Optional)

  • Statement of Research (Optional)

  • Statement of Teaching (Optional)

  • Statement of Contributions to Diversity (Optional)

  • Misc / Additional (Optional)

Reference requirements
  • 3 required (contact information only)