The Stankovic Laboratory at Stanford University School of Medicine seeks a highly motivated research scholar with expertise in data science, epidemiology, and computational analysis of large-scale electronic health record (EHR) data to join a dynamic, interdisciplinary team.
Requirements
- 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.
- Proficient in at least two of R, SAS, SPSS, or STATA.
- Skills in descriptive analysis, modeling of data, and graphic interfaces.
- Experience working with Spark/PySpark, SQL, or cloud-based analytic environments.
- Familiarity with propensity-score matching, survival analysis, or causal inference methods.
Responsibilities
- Design study. Extract, manage, and analyze large-scale EHR data within secure data environments (e.g., N3C Enclave and Cosmos).
- Develop and implement protocol for quality control. Develop reproducible code in Python and/or R for data wrangling, analysis, and visualization.
- Create analytic files with detailed documentation.
- Select appropriate statistical tools for addressing a given research question. Conduct and interpret statistical analyses including regression, survival, and longitudinal models.
- Implement data analysis through statistical programming.
- Present results for investigators using graphs and tables.
- Summarize findings orally and in written form.
Other
- Master's or PhD degree in a quantitative or biomedical field such as Epidemiology, Biostatistics, Data Science, Computer Science, Biomedical Informatics, or related discipline.
- Excellent written and verbal communication skills, with proficiency in English.
- Commitment to reproducible and transparent research practices.
- Interest in collaborative science.
- Outstanding ability to communicate technical information to both technical and non-technical audiences.
