Job Description
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 that showcase your current and previous coursework in social sciences and/or quantitative methods. 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., jobs, internships, research, coursework, teaching, etc.) that contribute to your qualifications. If the files you need to upload in 1 document exceed the size limit of the application portal, please send them directly to the QMSS Program Manager at [email protected].
Who We Are
The Quantitative Methods in the Social Science (QMSS) program in the College of Literature, Science, and the Arts at the University of Michigan aims to train undergraduate students in the theories and methods needed to be successful data literate social scientists. Todays job market is saturated 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 an applied and social science perspective. We are strongly committed to meeting students where they R [are], and providing ample time and resources to help them succeed so they can leave our courses and minor program with both competence and confidence in applied data and statistical skills for the social world.
Course Description
This Graduate Student Instructor (GSI) position in QMSS is a 50% effort position. There is 1 available position for Spring 2025. Between Winter 2021 and Winter 2025, QMSS received an average of 34 applications per term for all available GSI positions.
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: LSA GSI Class Size Policy.
Responsibilities*
Duties for this position includes, but is not limited to:
- Attend course lectures (6 hours/week).
- Teach 1 x 2-hour lab section each week.
- Dedicate at least 3 hours per week of office hours and/or individual meeting times to address student questions.
- 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 301 to be considered or qualified.
Desired Qualifications*
LSA doctoral students within their funding package. 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 preferred.
Modes of Work
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
For any questions, students can reach out to [email protected].
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
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.
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 https://oie.umich.edu/ and for the University Ombuds at https://ombuds.umich.edu/
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 employment opportunity employer.
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 that showcase your current and previous coursework in social sciences and/or quantitative methods. 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., jobs, internships, research, coursework, teaching, etc.) that contribute to your qualifications. If the files you need to upload in 1 document exceed the size limit of the application portal, please send them directly to the QMSS Program Manager at [email protected].
Who We Are
The Quantitative Methods in the Social Science (QMSS) program in the College of Literature, Science, and the Arts at the University of Michigan aims to train undergraduate students in the theories and methods needed to be successful data literate social scientists. Todays job market is saturated 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 an applied and social science perspective. We are strongly committed to meeting students where they R [are], and providing ample time and resources to help them succeed so they can leave our courses and minor program with both competence and confidence in applied data and statistical skills for the social world.
Course Description
This Graduate Student Instructor (GSI) position in QMSS is a 50% effort position. There is 1 available position for Spring 2025. Between Winter 2021 and Winter 2025, QMSS received an average of 34 applications per term for all available GSI positions.
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: LSA GSI Class Size Policy.
Responsibilities*
Duties for this position includes, but is not limited to:
- Attend course lectures (6 hours/week).
- Teach 1 x 2-hour lab section each week.
- Dedicate at least 3 hours per week of office hours and/or individual meeting times to address student questions.
- 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 301 to be considered or qualified.
Desired Qualifications*
LSA doctoral students within their funding package. 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 preferred.
Modes of Work
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
For any questions, students can reach out to [email protected].
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
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.
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 https://oie.umich.edu/ and for the University Ombuds at https://ombuds.umich.edu/
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 employment opportunity employer.
About University of Michigan
Why Work at Michigan?
Being part of something greater, of serving a larger mission of discovery and care — that's the heart of what drives people to work at Michigan. In some way, great or small, every person here helps to advance this world-class institution. It's adding a purpose to your profession. Work at Michigan and become a victor for the greater good.
Workplace Culture
The University of Michigan is committed to establishing, supporting and maintaining a culture where all members of the U-M community feel safe and supported.
Our Values
- Values guide our choices and actions. From how we hire and promote, make decisions, educate, conduct research, provide care, and treat one another. We strive to ensure the following values reflect who we are and what we stand for.
- Integrity - We act with honesty and take responsibility for our actions.
- Respect - We act in a way that acknowledges the humanity and contributions of each individual.
- Inclusion- We create an environment where all can participate, are invited to contribute, and have a sense of belonging.
- Equity - We create conditions that provide everyone an opportunity to thrive.
- Diversity - We welcome, acknowledge and appreciate our similarities and differences.
- Innovation - We promote creativity and curiosity to tackle challenges and inspire new ideas.
More about Working at U-M
- Our commitment to campus culture
- Our commitment to faculty and staff well-being
- Our commitment to employee engagement
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