Job Description

Responsible for planning data collection, and analyzing and interpreting numerical data from experiments, clinical studies and surveys; apply statistical methodology to provide information for scientific and clinical research; perform statistical analysis of data collected; prepare numerical information for presentations; prepare statistical sections of research protocols and scientific publications; utilize experiment design, sampling theory, simulation study, Python, large medical claim data, categorical data analysis, Cox PH, shrinkage regression, LASSO, ENET, adaptive ENET, tree-based algorithms, CART, random forests, inverse probability weighting, propensity score weighting, Linux, Git, ggplot2 (data visualization tool), categorical analysis, survival analysis, longitudinal analysis, power analysis, missing data analysis, statistical simulation, mediation analysis, weighting methods, epidemiology, machine learning, Excel VBA, R, R-Shiny, Python, data wrangling, data visualization, Hypothesis testing, Linear Regression Modeling, Logistic Regression Modeling, Residual Analysis, SAS and random/mixed effects models to perform duties; evaluate reliability of source information, adjust and weigh raw data, and organize results into a form compatible with analysis by computer or other methods; assist investigators with the design of complex studies by planning methods to collect information and by developing questionnaires and sampling techniques; analyze data using diverse statistical methods from univariate statistics to complex multivariate techniques, as well as utilizing appropriate statistical packages; present numerical information in the form of customized tables and figures by advanced tools; and assist investigations with the designing of research studies or projects.

Location : Detroit, Michigan and multiple undetermined worksites throughout the US .

"DNS"

#LI-DNI"

Education: Masters Degree in Biostatistics, Statistics, Applied Statistics, or in a related field of study (will accept equivalent foreign degree);

Training: None.

Experience: None.

Other Requirements: Must have completed one (1) academic semester of coursework or research work that included use of all the following: Excel VBA, R, R-Shiny, Python, data wrangling, data visualization, Hypothesis testing, Linear Regression Modeling, Logistic Regression Modeling, Residual Analysis, SAS and random/mixed effects models.

Will also accept any suitable combination of education, training and/or experience. Henry Ford Health System

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