Research - Laboratory/Non-Laboratory, Staff/Administrative
A cover letter is required for consideration for this position and should be attached as the first page of your resume. The cover letter should address your specific interest in the position and outline skills and experience that directly relate to this position.
The Center for the Management of Information for Safe and Sustainable Transportation (CMISST) group at the University of Michigan Transportation Research Institute is seeking a part-time (20 hrs/week) statistician to join a multidisciplinary team focused on gathering, linking, disseminating, and analyzing transportation and related datasets, as well as developing new statistical techniques for analysis of linked datasets.
NOTE: the salary range of $55,000-$65,000 would be the full-time rates for this position.
Independently conduct statistical analyses of transportation data using established techniques.
Provide guidance on appropriate statistical techniques to researchers.
Perform statistical simulations to assess new statistical techniques.
Document the methods and results of statistical analyses in reports, journal articles, presentations, and other visual media.
Query large databases.
Perform literature reviews, consult with faculty to define research questions and analytical design.
Write, test, and implement programs to clean, merge, and analyze large, complex datasets.
Create standardized and recoded variables to facilitate analyses using multiple datasets.
Implement methods to ensure data quality.
Participate as a team member in discussions on data analysis and improvement of data collection, quality of data analyses, programming, and documentation.
The ideal candidate will have completed a master’s degree in biostatistics, statistics or other related field.
1-3 years relevant experience in statistical data analysis, modeling, programming, and simulation.
Expertise in programming and analysis with SAS or R.
Demonstrated ability to effectively document and communicate statistical methods.
Experience with complex data structures and linkages between data sources
Experience with GIS
Familiarity with SQL.
Familiarity with Python.
Some experience with analysis of Big Data.
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Internal Number: 201186
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