GSI for Winter 2027 (QMSS 301)

University of Michigan; Ann Arbor (MI); Hybrid; USD 28,000 - 50,000; Part time

Job summary

The University of Michigan is seeking a Graduate Student Instructor (GSI) for QMSS 301 - Quantitative Social Science Analysis and Big Data. The role involves leading lectures and weekly lab sections, holding office hours, and collaborating with teaching teams to design assignments and provide feedback.

Candidates must be a graduate student in good standing, enrolled in at least six credit hours, and proficient with analytics tools (Excel, Tableau, Stata, R, Python).

Qualifications

• Proficiency with analytic tools (Excel, Tableau, Stata, R, Python, etc.) is required.
• Experience with social sciences research, coursework, and/or perspectives as they pertain to data analysis, interpretation, and communication is required.

Responsibilities

• Attend course lectures (3 hours/week).
• Teach 3 x 1-hour lab sections each week.
• Dedicate at least 2 hours per week of office hours and/or individual meeting times to address student questions.
• Attend weekly QMSS Community Hours events throughout the semester for a total of 19.5 hours over the course of the semester.
• 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.

Skills

Excel

Tableau

Stata

R

Python

Education

Graduate student status

Job description

Responsibilities*

Mandatory duties for these positions include, but are not limited to:

Attend course lectures (3 hours/week).

Teach 3 x 1-hour lab sections each week.

Dedicate at least 2 hours per week of office hours and/or individual meeting times to address student questions.

Attend weekly QMSS Community Hours events throughout the semester for a total of 19.5 hours over the course of 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, GSIs from each QMSS 201 and QMSS 301 course will be expected to be present for possible student questions.

Community Hours will be scheduled once per week, alternating between Monday, Tuesday, or Wednesday evenings 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, 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 301 to be considered or qualified.

Experience with social sciences research, coursework, and/or perspectives as they pertain to data analysis, interpretation, and communication is required.

Desired Qualifications*

LSA doctoral students within their funding package are preferred. A mastery of quantitative methods in the social sciences, pursuing a graduate degree in a quantitative methods- or social sciences-related field, and real-world job/internship, 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. A strong commitment to serving as a resource for undergraduate students and ability to make quantitative methods in the social sciences accessible to students of all levels of previous experience (including none at all) is strongly preferred.

Course Description

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 learn web scraping, geospatial analysis, text-based analysis, and predictive analysis. Students will be taught and asked to use R and Python in this course.

For information about class size, please refer to the LSA GSI Class Size policy here:

Positions that are eligible for hybrid or mobile/remote work mode are at the discretion of the hiring department. Work agreements are reviewed annually at a minimum and are subject to change at any time, and for any reason, throughout the course of employment. Learn more about the work modes .

Contact Information

Contact Regina-Loar Clark at [e-mail] with questions. Applications are due by November 24, 2026, and offers will be extended by December 14, 2026.

Decision Making Process

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

Selection Process

Only fully complete applications following the application instructions will be reviewed. Top candidates of interest based on their submitted applications will be invited for a behavioral interview with QMSS faculty, which will take place either in-person or remotely over Zoom. Candidates selected for an offer by the QMSS Director will demonstrate the necessary skills, experiences, and perspectives to succeed as a QMSS Graduate Student Instructor.

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.

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 https://spg.umich.edu/policy/601.38 , and its relation to your employment can be found in MOU 10 of your employment contract.

U-M EEO Statement

The University of Michigan is an Equal Opportunity Employer. We are committed to providing an environment of mutual respect where equal employment opportunities are available to all applicants, including protected veterans and individuals with disabilities.

Job Opening ID

285482

Job Title

GRAD STU INSTR

Modes of Work

Onsite

Full/Part Time

Part-Time

Regular/Temporary

Regular

FLSA Status

Exempt

Organizational Group

College Of Lsa

Posting Begin/End Date

11/10/2026 - 11/24/2026

Graduate Students
Graduate Student Instructors (GEO)