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How to Apply

Applicants must submit a cover letter, curriculum vitae (CV) or resume, copies of previous teaching evaluations (if applicable), and a copy of your unofficial transcript(s) from current and/or previous institutions. In your cover letter, please explain why you would like to GSI for QMSS (including which course(s), if applicable) and the skills (e.g., analytical tools such as Excel, R, Tableau, Python, etc.) and experiences (e.g., research, coursework, relevant work experience, teaching, etc.) that contribute to your qualifications.

Who We Are

The Quantitative Methods in the Social Science (QMSS) program aims to train undergraduate students in the theories and methods needed to be successful data literate social scientists by the time they graduate from the University of Michigan. 

The job market is booming with opportunities that either desire or require skills in data literacy ? whether that means being able to find data, analyze data, or know how and when to use data ? and this is true even for jobs outside of the data science or analyst fields specifically. 

QMSS was designed to teach students how data can be used to generate solutions for social problems of today and tomorrow and give students opportunities to apply and practice their skills to hit the ground running in their internships and careers in the future. QMSS is unique relative to programs in statistics or data science in that we teach data-based skills from a social science perspective. 

Course Description

Graduate Student Instructor (GSI) positions in QMSS are 50% effort positions. There are 2 available positions for Winter 2024. Between Winter 2021 and Fall 2023, QMSS received an average of 20 applications for available GSI positions.

QMSS 201 - Introduction to Quantitative Methods in the Social Sciences:
This course includes training in descriptive statistics, data collection, data management, and data cleaning. It provides an overview of research design and hands-on experience with using data to ask and answer research questions, and educates students about ethical issues around data, data analysis, and reporting. Students will be taught and asked to use Excel, Tableau, and R in this course.

QMSS 301 - Quantitative Social Science Analysis and Big Data:
This course will cover methodological approaches to answering social questions that combine
theory and skills from social science, social research methodology, and ?big data? techniques. Topics of discussions will include developing social science questions and identifying, accessing, managing, and analyzing data that can inform those questions. Students will be taught and asked to use R and Python in this course.


Duties for these positions include, but are not limited to: 

  • Attend course lectures (3 hours/week) 
  • Teach 2 x 1-hour lab sections each week. Lab times are scheduled so that GSIs may be able to teach 2 back-to-back sections in the same room for ease of planning, though this is not guaranteed. 
  • Hold at least 3 hours per week of office hours to address student questions. QMSS has desk space to conduct office hours in Weiser Hall.
  • Attend a minimum of 7 QMSS Community Hours events throughout the semester. Community Hours are designed to be a supplement to traditional office hours during which students from all QMSS courses can come to work independently or in groups (depending on the rules of given assignments) on problem sets, projects, and/or exam studying. During Community Hours, 1-2 GSIs from each QMSS 201 and QMSS 301 course will be expected to be present for possible student questions. You may be assisting students who are not enrolled in your lab section. QMSS Community Hours will be scheduled once per week on a Monday, Tuesday, or Wednesday evening from 6pm - 9pm.
  • Participate in weekly teaching team meetings.
  • Assist with software/tools/datasets.
  • Co-create problem sets and other assignments.
  • Grade and provide constructive feedback on assignments and projects.
  • Participate in QMSS program activities.

Required Qualifications*

To be appointed as a GSI or GSSA, a graduate student must be in good standing in their degree program and for Terms I and II, must be registered for not less than six (6) credit hours. With written approval of the student's faculty advisor, five (5) credit hours may be acceptable.

Proficiency with analytic tools (Excel, Tableau, Stata, R, Python, etc.) is required. You do not have to be proficient in all analytic tools associated with QMSS 201 or QMSS 301 to be considered or qualified.

Desired Qualifications*

LSA student enrolled in a graduate program. A mastery of quantitative methods in the social sciences, pursuing a graduate degree in a quantitative methods- or social sciences-related field, and teaching and/or research experience that will help in teaching undergraduate students how to perform quantitative analyses using common analytical tools in order to answer social science questions is desired.

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.

Contact Information

For any questions, students can reach out to

Decision Making Process

The Director of Quantitative Methods in the Social Sciences awards all GSI positions based on stated qualifications, criteria, and academic discretion.

Selection Process

Priority will be given to LSA doctoral students within their five year funding commitment, with a preference for students pursuing degrees in a social science discipline.   

GEO Contract Information

The University will not discriminate against any applicant for employment because of race, creed, color, religion, national origin, ancestry, genetic information, marital status, familial status, parental status or pregnancy status, sex, gender identity or expression (whether actual or perceived), sexual orientation, age, height, weight, disability, citizenship status, veteran status, HIV antibody status, political belief, membership in any social or political organization, participation in a grievance or complaint whether formal or informal, medical conditions including those related to pregnancy, childbirth and breastfeeding, arrest record, or any other factor where the item in question will not interfere with job performance and where the employee is otherwise qualified. The University of Michigan agrees to abide by the protections afforded employees with disabilities as outlined in the rules and regulations which implement Section 504 of the Rehabilitation Act of 1973 and the Americans with Disabilities Act.

Information for the Office for Institutional Equity may be found at and for the University Ombuds at

Unsuccessful applications will be retained for consideration in the event that there are last minute openings for available positions. In the event that an employee does not receive their preferred assignment, they can request a written explanation or an in-person interview with the hiring agents(s) to be scheduled at a mutually agreed upon time.

This position, as posted, is subject to a collective bargaining agreement between the Regents of the University of Michigan and the Graduate Employees' Organization, American Federation of Teachers, AFL-CIO 3550.

Standard Practice Guide 601.38, Required Disclosure of Felony Charges and/or Felony Convictions applies to all Graduate Student Assistants (GSAs). SPG 601.38 may be accessed online at , and its relation to your employment can be found in MOU 10 of your employment contract.

U-M EEO/AA Statement

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