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An established industry player is seeking a skilled Biostatistician to join their Department of Population Science and Policy. This role involves leveraging data to enhance health outcomes in rural areas through advanced methodologies and statistical programming. You will collaborate with various teams, contribute to research grants, and mentor students in the Master of Science program. The ideal candidate will possess a Ph.D. or equivalent in a relevant field and demonstrate proficiency in statistical software. Join a dynamic team dedicated to improving community health through innovative data analysis and research.
This position within the Department of Population Science and Policy offers expertise in database creation and maintenance, statistical programming, study design, and advanced methodologies for population science research at SIU School of Medicine. The Biostatistical/Bioinformatic support focuses on quantitative, epidemiologic, and outcomes research, including big data analysis. Key areas include: analysis of Illinois Medicaid database, other large datasets (NHANES, BRFSS), creation of a data warehouse from electronic health records, analysis of ambulatory clinic data, obtaining service agreements for dataset analysis, and supporting grant development. The Data Analytics Unit aims to conduct high-quality scholarly work to improve regional health, leveraging data acquisition, integration, and large dataset analysis to enhance rural and small-town health outcomes. The role requires strong quantitative skills, experience with large datasets, and the ability to communicate findings effectively to scientific and community audiences. Collaboration and teamwork are essential.
- Ph.D. in biostatistics, bioinformatics, epidemiology, health services research, or related field, or M.D./D.O./D.N.P. with a master's in relevant areas.
- Sensitivity to the needs of underrepresented and rural populations.
- Proficiency in multiple methodologies including data imputation, survival analysis, structural equation modeling, and prediction model development.
- Experience with longitudinal clinical datasets and large epidemiological datasets.
- Competence in at least one statistical software (SAS, STATA, R, SPSS).