Doctoral Students
The Center for Statistical Training and Consulting (CSTAT, https://cstat.msu.edu) will be interviewing to fill Research Assistant (RA) positions for Summer 2024 (05/16/2024–08/15/2024), and Fall 2024 (08/16/2024–12/31/2024), with possible continuation into future semesters depending on performance, staffing needs, and available funding. Doctoral students with strong quantitative methods, statistics, and research design skills are invited to apply.
CSTAT is located in Giltner Hall. Our aim is to improve the quality of research produced by the MSU academic community. We provide graduate students who have statistical and computational expertise the opportunity to work with researchers across many MSU departments. Case studies can be found at https://cstat.msu.edu.
Graduate students who become involved in CSTAT are provided with unique opportunities to apply professional expertise from their course work to applications in a supervised learning environment. Through working with an experienced mentor and discussions in weekly staff meetings, our research assistants can bridge the gap between classroom training in statistical methods knowledge and methods used in practice (“knowledge transfer”). They can also learn effective communication and collaborative skills. A statistical collaborator fulfills multiple roles in a research collaboration including as expert, communicator, leader, and advocate. Thus, the qualities of integrity, reliability, and respectfulness toward others and oneself are essential character traits.
Job Description
A half-time (50%) RA in CSTAT works 20 hours per week during each semester of employment, while a quarter-time (25%) RA would work 10 hours per week instead. Fall and Spring employment periods both last about 19 weeks, while the Summer employment period lasts 13 weeks. Other levels of employment are also possible (e.g., hourly or volunteer, see application form). A successful applicant will be offered a half- or quarter-time RA, or an hourly RA position depending on which arrangement is most viable for the student and CSTAT.
The RA will collaborate with, and provide statistical services to, MSU graduate students, faculty, and research staff and possibly to occasional external clients. The job involves a variety of activities, including: helping clients with research design issues (e.g., power analysis/sample size planning, missing data); helping clients with data management and data cleaning; identifying analyses that are appropriate for answering clients' research questions; reviewing the methods literature; rapidly learning new methods and software; recommending relevant resources and citations to clients; explaining statistical models to clients; demonstrating how to use statistical software; interpreting statistical output; and suggesting effective ways to present results in manuscripts and presentations. Research assistants may be asked to analyze data for clients, co-author manuscripts, presentations, or other scholarly products, and may prepare small bids/proposals for clients who are expected to pay for services. Training and mentoring will be provided by CSTAT personnel.
Requirements
- Applicant must be a currently enrolled doctoral student and must already have a Master’s degree (or similar training/experience) by the start of the RA appointment.
- Ability to match clients’ research questions and hypotheses to appropriate statistical methods.
- Experience with cleaning and managing data.
- Experience performing statistical analyses and interpreting the results.
- Familiarity with a range of statistical methods and with statistical software packages.
- Strong oral and written communication skills are essential.
- Ability to prioritize tasks and accomplish work on multiple projects that may have competing deadlines.
How to Apply
Please e-mail all of the following to cstatinf@msu.edu by 5:00 PM on Friday, 2/16/2024:
- Cover letter
- Resume or curriculum vitae (CV): Make sure that you list all of your research experience, publications, & presentations.
- CSTAT RA Application Form
