Biostatistician I

Under general direction from senior staff and faculty, will be responsible for evaluating reliability of source information, adjusting and weighing raw data, and organizing results into a form compatible with analysis by statistical software or other methods; utilize programming practices to support robust and transparent research, including modular programming, direct output of results, and version control, using the SAS and R programming environments; develop functions and macros to accomplish repetitive tasks; conduct data management, quality control exercises, and database reporting; merge, transform, and filter multiple related data files to create analysis-ready datasets; analyze and interpret numerical data from experiments, studies, surveys, large clinical trials, and other sources; perform statistical analysis using a variety of methods from simple univariate statistics to multivariate techniques and with the use of statistical software; use robust practices in SAS, R and foundational biostatistical methods, including bivariate testing and multi-variable regression modeling of binary, count, continuous, and censored outcomes, to conduct planned data analysis; utilize models that account for correlated data, including repeated measures over time; prepare numerical information for presentation in the form of statistical software output, graphs, charts, tables, written reports, and other methods; follow standard memo templates to document data preparation and analysis results, including clinically relevant interpretations of the analyses to address the research hypothesis in reports; prepare statistical sections of research grants and scientific publications for less complex studies; assist investigators with the design of less complex studies by planning methods to collect information and by developing questionnaires and sampling techniques; utilize and apply knowledge of statistical consulting, ICD code, CPT code, regular expression, medical claim data, manuscript drafting, survival analysis, propensity score, mediation analysis, causal inference, nonparametric statistics, variable selection, missing data, clinical trials, power analysis, statistical simulation, longitudinal studies, and multivariable regression for continuous/binary/count/time-to-event outcomes to perform assigned duties; understand and abide by research regulations, including practicing Responsible Conduct of Research, maintaining data security, performing dataset de-identification, and conducting project tracking for funding compliance and billing; employ time management strategies to accomplish work goals on multiple studies, meeting agreed upon deadlines; and communicate effectively with medical and research faculty about scope of work, analysis goals, and timelines.

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

"DNS"

#LI-DNI"

Education: Masters Degree in Biostatistics, Statistics, Engineering, 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: propensity score, mediation analysis, causal inference, nonparametric statistics, variable selection, missing data, clinical trials, power analysis, statistical simulation, longitudinal studies, and multivariable regression for continuous/binary/count/time-to-event outcomes.

Will also accept any suitable combination of education, training and/or experience.