The Francis I. Proctor Foundation at the University of California, San Francisco is seeking a Biostatistician in the Academic Specialist job series (staff with full benefits) to help lead statistical research efforts in its Data Coordinating Center (DCC). The Proctor Foundation's mission is to prevent blindness worldwide through research and teaching (https://proctor.ucsf.edu/), and it is one of the world's leading clinical trial centers for eye-related intervention studies. Proctor faculty currently lead more than 20 randomized controlled trials throughout the world, funded by the National Institutes of Health and the Bill & Melinda Gates Foundation. Trials range in scale from hundreds- to hundreds of thousands of participants, and key areas of research include: interventions to support trachoma elimination, community-based screening to identify chronic eye disease, therapies to control uveitis, corneal surgery trials, and mass drug administration to reduce all-cause child mortality. Many trials use innovative designs, such as response-adaptive allocation and group-sequential testing. In addition to randomized controlled trials, many faculty lead large-scale observational studies using electronic health records data that include millions of people.
The Biostatistician will play a lead role in the DCC in the design and analysis of clinical trials, working in close collaboration with Proctor faculty and analysts. The Biostatistician will join an exceptional team at Proctor, with latitude to lead methodologic research in clinical trial design and analysis topics, drawing on Proctor's extensive past and ongoing trials. The position is ideally suited for people with PhD-level training in biostatistics or statistics, but we will consider exceptional candidates with a combination of a master's degree and relevant experience working on clinical trials or field trials.
Key responsibilities in the DCC will include:
Work with faculty on study design (e.g., sample size, sampling approaches)
Develop statistical analysis plans for trials
Lead interim and primary endpoint analyses for trials
Work with faculty and data and safety monitoring committees to monitor trials, including closed (masked) interim efficacy and futility analyses
Work with- and mentor/advise analysts to develop:
reproducible, computational workflows
automated study monitoring reports
database and electronic data collection design
secondary outcome analyses for trials
Appointees in the Specialist series will be expected to engage in specialized research, professional activities and do not have teaching responsibilities. Specialists are expected to use their professional expertise to make scientific and scholarly contributions, and may participate in University and Public Service. Screening of applicants will begin immediately and will continue as needed throughout the recruitment period. Salary and rank will be commensurate with the applicants experience and training.
Specialists appointed at the Assistant or Associate rank must possess (or in process of obtaining) a Master's degree in biostatistics, statistics, or related discipline plus at least 4 year's experience working as a statistician on prospective clinical trials.
Specialists appointed at the full rank must possess (or in process of obtaining) a PhD in biostatistics, statistics or a related discipline plus at least 2 year's experience working on prospective clinical trials.
Applicants must have obtained the degree requirement for the Specialist rank by the time of hire.
Familiarity with epidemiologic trial design and conduct.
Strong analytic skills: should be adept at programming and data analysis (preferably R).
Experience leading small teams (<10) of masters-level statisticians or data scientists.
Good communication (writing and speaking).
Publication records commensurate with experience, with an emphasis on collaborative contributions.
Applicant's materials must list (pending) qualifications upon submission.
4 or more years of experience working as a statistician on prospective clinical trials.
Experience with causal inference methods (e.g., DAGs, structural causal models), and transparent/ reproducible data science tools (e.g., GitHub, R markdown).
Experience and/or interest in international research.
UC San Francisco seeks candidates whose experience, teaching, research, or community service that 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.