A cover letter is required for consideration for this position. The cover letter should address your specific interest in the position and outline skills and experience in teaching quantitative methods that directly relate to this position and the QMSS courses described. A complete application will include all of the following documents:

• a cover letter,
• curriculum vitae,
• a teaching statement: a document with a narrative describing your teaching philosophy and experience,
• a quantitative methods experience statement: a document with a narrative detailing your experience with quantitative methods,
• evidence of teaching excellence (student evaluations of teaching - scores and student comments),
• three letters of recommendation will also be required and must be submitted within 10 days of the application. 

The Quantitative Methods in the Social Sciences (QMSS) program seeks applicants for a part-time Lecturer I position with an anticipated start date of August 25, 2025. This is a non-tenure track position with two consecutive appointment periods for the Fall 2025 and Winter 2026 terms (i.e., August 25, 2025 to December 31, 2025 and January 7, 2026 to April 30, 2025) with the possibility of renewal.

This part-time (33% or 66% appointment effort) lecturer will teach existing courses developed by or in consultation with other QMSS faculty and/or the QMSS director, including QMSS 201: Introduction to Quantitative Methods in the Social Sciences and/or QMSS 301: Quantitative Social Science Analysis and Big Data. A 33% appointment requires teaching 1 course per semester, and a 66% appointment requires teaching 2 courses per semester. Courses will be assigned to the lecturer by the QMSS Director based on program needs, area(s) of expertise, and technical skills.

In particular, we are looking for an instructor to deliver both didactic and hands-on experiential learning in quantitative methods to undergraduate students with a wide range of statistical and mathematical backgrounds.

QMSS 201 includes training in descriptive statistics, data collection, data management, data cleaning, data ethics, and data communication drawing from various social science theories and perspectives. It provides an overview of research design and hands-on experience with using data to ask and answer research questions applicable to both academic research and other, real-world use cases.

QMSS 301 includes training in methodological approaches to answering social questions that use or require 'big data' techniques such as web scraping, text-based analysis, geospatial analysis, and predictive analysis. Topics of discussions will include developing social science questions and identifying, accessing, managing, and analyzing data that can inform those questions. 

The mission of the University of Michigan is to serve the people of Michigan and the world through preeminence in creating, communicating, preserving and applying knowledge, art, and academic values, and in developing leaders and citizens who will challenge the present and enrich the future.

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, communicate data, know how and when to use data, or understand information based on 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 and through application-based teaching and learning.

The lecturer will teach either 1 (33% appointment) or 2 (66% appointment) QMSS courses (QMSS 201 and/or 301) per semester. Courses are assigned by the QMSS Director and based on the lecturers expertise and technical skills. We are seeking applicants who are able to teach at least QMSS 301.

QMSS 201: Introduction to Quantitative Methods in the Social Sciences is a 4-credit course with a lecture (3 hours/week) and laboratory (1 hour/week) component. The lecturer will develop and deliver all content, projects, and assessments for the lecture component of the course. The laboratory component of the course is taught by section instructors, typically Graduate Student Instructors (GSIs). The lecturer is responsible for managing, mentoring, and assigning duties to the section instructors, including creating and grading hands-on assignments and projects for the laboratory component. Lecturers teaching QMSS 201 must include content, assignments, and/or projects using Excel, Tableau, and R with applications in many different social science areas.

QMSS 301: Quantitative Social Science Analysis and Big Data is a 4-credit course with a lecture (3 hours/week) and laboratory (1 hour/week) component. The lecturer will develop and deliver all content, projects, and assessments for the lecture component of the course. The laboratory component of the course is taught by section instructors, typically Graduate Student Instructors (GSIs). The lecturer is responsible for managing, mentoring, and assigning duties to the section instructors, including creating and grading hands-on assignments and projects for the laboratory component. Lecturers teaching QMSS 301 must include content, assignments, and/or projects using R and Python. QMSS 301 content includes training in web scraping, text-based analysis (e.g., sentiment analysis), geospatial analysis, and predictive analysis with applications in many different social science areas.

Additional teaching and instructional responsibilities for all courses include holding office hours for students, individual and small group tutoring and project assistance, course planning and grading, and development of labs and instructional tools and resources related to quantitative social science research and analysis.

A PhD in a social science discipline with a demonstrated focus on quantitative methods is required. Experience teaching courses using quantitative methods and analysis is required. Demonstrated proficiency in teaching and/or using multiple analytical tools and approaches (e.g., Excel, Tableau, R, Python, SQL, STATA, etc.) is required.

Successful candidates will have contemporary knowledge and experience in applying data analysis and data science skills to social science-oriented research and/or teaching, with experience teaching undergraduate students strongly preferred.

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.

This position is covered under the collective bargaining agreement between the U-M and the Lecturers Employee Organization, AFL-CIO, which contains and settles all matters with respect to wages, benefits, hours and other terms and conditions of employment.

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.

Questions about applying for this position can be emailed to: qmss.program@umich.edu.

Anticipated application deadline is July 31, and we aim to schedule interviews in the following 1-2 weeks. The offer to the final candidate is anticipated to be made before August 20, 2025

The University of Michigan is an equal employment opportunity employer.