Data Science Consultant

How to Apply

A cover letter and resume are required for consideration for this position; include the cover letter as the first page of your application. The letter should specifically address your interest in this position and outline relevant skills and experience.

Summary

Consulting for Statistics, Computing, and Analytics Research (CSCAR) is a service unit within the University of Michigan's Office of the Vice President for Research, supporting U-M researchers in all activities involving quantitative data analysis. CSCAR consultants work directly with UM investigators, including faculty, students, and staff, engaged in a wide range of research areas.

CSCAR seeks a Data Science Consultant to serve as a member of its core staff. The ideal candidate will be an excellent communicator with advanced statistical training and computing expertise, holding a graduate degree in Statistics, Biostatistics, Data Science, or Applied Mathematics. CSCAR consultants frequently encounter analytic challenges that involve advanced statistical methods.

Thus, a solid grounding in statistical theory and evidence of creative statistical insight are essential. The work may require development and implementation of innovative methods and computation involving data that exhibit complex and unconventional structure. Thus, a consultant should have facility with a variety of computational tools including fluent data programming skills and an understanding of numerical algorithms used in data modeling and analysis. CSCAR consultants must be capable of managing multiple projects and responsibilities simultaneously and should be able to set and meet goals with minimal supervision.

Responsibilities*

Collaborate with University of Michigan researchers.

Hold one-on-one consulting appointments with U-M researchers.

Serve as an instructor for workshops in a classroom setting and develop workshop materials.

Respond to questions sent to CSCAR via email.

Conduct statistical analyses for CSCAR clients.

Mentor and supervise graduate students working at CSCAR.

Required Qualifications*

A graduate degree in Statistics, Biostatistics, Data Science, or Mathematics.

A solid foundation in the theory of statistical data analysis.

Teaching and/or consulting experience at the university level.

Excellent computing skills, including facility with two or more computing environments commonly used for data analysis (e.g. SAS, SPSS, R, Julia, Stata, Matlab), programming experience in one or more common programming languages (e.g. C or Python), and facility with advanced computing environments including clusters and distributed data and computing infrastructure.

Excellent interpersonal skills and the ability to communicate advanced technical concepts clearly and effectively.

Strong background and interest in research design, and an interest in the broader goals of research in diverse fields.

A positive attitude toward continuously learning new skills.

A positive attitude toward helping researchers meet their goals.

Background Screening

The University of Michigan conducts background checks on all job candidates upon acceptance of a contingent offer and may use a third party administrator to conduct background checks. Background checks are performed in compliance with the Fair Credit Reporting Act.

Application Deadline

Job openings are posted for a minimum of seven calendar days. The review and selection process may begin as early as the eighth day after posting. This opening may be removed from posting boards and filled any time after the minimum posting period has ended.

U-M EEO/AA Statement

The University of Michigan is an equal opportunity/affirmative action employer.

Job Detail

Job Opening ID

251104

Working Title

Data Science Consultant

Job Title

Statistician Senior

Work Location

Ann Arbor Campus

Ann Arbor, MI

Full/Part Time

Full-Time

Regular/Temporary

Regular

FLSA Status

Exempt

Organizational Group

Umor Cscar

Department

UMOR Cnslt Stat,Comp&Anlyt Res

Posting Begin/End Date

7/01/2024 - 7/22/2024

Career Interest

Research

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