About
Develop reproducible code in Python and/or R for data wrangling, analysis, and visualization. Extract, manage, and analyze large-scale EHR data within secure data environments (e.g., N3C Enclave and Cosmos). Implement epidemiologic study designs (e.g., retrospective cohort, target trial emulation, propensity-score matching). Conduct and interpret statistical analyses, including regression, survival, and longitudinal models. Collaborate on manuscript writing, figure preparation, and presentation of findings for ongoing projects in the lab. Contribute to grant proposals and new analytic frameworks. Engage in interdisciplinary collaboration with hearing and balance researchers, physicians, and epidemiologists. Qualifications:
Required:
Master's or PhD degree in a quantitative or biomedical field such as Epidemiology, Biostatistics, Data Science, Computer Science, Biomedical Informatics, or related discipline. A Bachelor's degree plus 3 years of relevant experience will also be considered. Strong proficiency in Python and/or R for data analysis and statistical modeling. Experience with EHR, large-scale health data (OMOP, PCORnet, or similar), or real-world databases (e.g., Optum, IQVIA, Merative, Medicare/Medicaid, etc.) Understanding of epidemiologic study design, bias, and confounding. Excellent written and verbal communication skills, with proficiency in English Commitment to reproducible and transparent research practices. Interest in collaborative science. Preferred:
Experience working with Spark/PySpark, SQL, or cloud-based analytic environments. Familiarity with propensity-score matching, survival analysis, or causal inference methods. Prior exposure to biomedical or neuroscience applications (hearing or sensory research not required). To Apply: Please send the following materials in a single PDF to Rachel McGowan (rmcgowan@stanford.edu) and Dr. Shelley Batts (sbatts@stanford.edu) with the subject line Application - Data Scientist:
Cover letter describing your background, programming experience, and research interests. Curriculum vitae. Contact information for three references. (Optional but encouraged) A link to a GitHub repository, portfolio, or example of prior analytic work demonstrating programming or data analysis skills. Applications will be reviewed on a rolling basis until the position is filled. Start date is flexible.
Languages
- English
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