Biostatistician I

Indiana University - School of Public Health
Indiana, United States
Salary Not specified
Oct 16, 2020
Employment Level
Non-Tenured Track
Employment Type
Full Time

Employer:                             Indiana University


Job Location:                        Bloomington, Indiana


Job Title:                                Biostatistician I


Job Duties:  Provide biostatistics consultations to faculty and researchers performing health-related research.  Perform data science techniques with advanced programming and data management in multiple software (SAS, R, Python, SQL, Stata, Matlab, SPSS), process big data, and utilize machine learning (ML) methods.  Perform genetic/genomic data management (including quality checking, trimming, alignment and quantification) and analysis including ML techniques on high-performance computing (HPC) systems.  Perform meta-analysis and systematic review for biomedical research.  Collaborate with faculty researchers to provide statistical programming, data analysis, interpret analysis results, write analytical portions of manuscripts based on methods and results of analysis, and address manuscript revisions based on reviewer comments.  Support investigators in grant development including statistical design of studies, sample size and power calculations (data simulations), and preparation of analysis plans of grant proposals.

Requirements:  Master’s degree in Statistics or related field.  2 years of statistical data management and analysis experience.  Experience in health-related research.  SAS certification.  Experience in R/RStudio, Python, SQL, Stata, Matlab, SPSS, Latex, Tableau, R ggplot2, SAS SGPLOT, Linux/Unix environments, high-performance computing, CNN, KCF, random forest, SVM, BKMR, mean shift, Monto Caro, permutation, bootstrap, jackknife, G*Power, and PASS.  Experience in designing, collecting, developing and implementing databases (SQL), as well as data cleaning and manipulation.  Experience in statistical methods for dimensionality reduction, longitudinal data analysis, survival analysis, time series analysis and casual inference.

                Interested candidates should apply at