Kaiser Permanente – Biostatistician

Company Information: The Division of Research of Kaiser Permanente, Northern California seeks to recruit a doctoral level biostatistician with expertise and experience in statistical learning and its interface with causal inference methods for complex longitudinal or dependent network data. Investigators in the Division of Research (DOR) draw on rich clinical data derived from a comprehensive state-of-the-art electronic medical record and enjoy direct access to a diverse population of >4.1 million members afforded by its location within Kaiser Permanente Northern California. The group has more than 50 research scientists based in Oakland who lead well-developed programs in cardiovascular and metabolic diseases, health care delivery and policy, behavioral health and aging, cancer, infectious diseases, and women’s and children’s health. DOR investigators are primarily supported by grant and contract funding from federal and other external sources, especially the National Institutes of Health.
DOR is an Equal Employment Opportunity/Affirmative Action employer and strongly encourages women and applicants of color to apply.

Position Title: Biostatistician

Duties and Responsibilities: This position is similar to a faculty position in an academic research institution. Responsibilities include collaboration on multidisciplinary research teams by providing expertise in study design and statistical methods, preparing proposals and manuscripts, and guiding master’s level analysts. The successful candidate is expected to work with other DOR scientists on content and methodologic issues, collaborate with physicians and program leaders within Kaiser Permanente, and develop national and international collaborations to advance research in their respective methodological area. The position also provides opportunities to lead independent research.

Position Qualifications: The individual must have a proven track record of developing, implementing, and automating real data analyses with machine learning and causal inference methodologies to tackle a broad range of applied research problems in studies based on large healthcare databases. The ideal candidate will be familiar with deep-learning, high performance cloud computing and GPU programming.
The candidate must have: (i) a PhD in biostatistics, statistics, or a related field, (ii) established track record of successful statistical software development and engineering, with real applications to complex large scale analyses, (iii) demonstrated expertise in modern nonparametric statistical methods (machine learning) and their applications in causal inference, (iv) demonstrated history of successful collaboration and consultation, (v) a track record of peer-reviewed scholarly publications, and (vi) excellent oral and written skills.

Salary Range:

Benefits:

Website: https://divisionofresearch.kaiserpermanente.org/

Application Information: Please send a letter of interest and curriculum vitae to Monica Sokil (Monica.Sokil@kp.org).

Contact Email: Monica.Sokil@kp.org

Application Deadline: 06/04/2018