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 letters should be made out to Ariel Hasell, Director of QMSS, Associate Professor of Communication and Media.

The Quantitative Methods in the Social Sciences (QMSS) program seeks applicants for a full-time Lecturer III position with an anticipated start date of August 25, 2026. This is a non-tenure track position, with a University Year appointment period (i.e., runs August 25 through May 24 each year).

The initial appointment period will be four (4) years and the appointment may be renewed (based on programmatic need, funding, and performance). The appointment is subject to final approval by the college.

A typical full-time (100% effort) load for a Lecturer III in the College of Literature, Science, and the Arts is three (3) courses per semester, or the equivalent in administrative and/or service duties. QMSS Lecturer IIIs teach five (5) courses per academic year, with one course reduction for administrative and/or service duties for the program.

This 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, QMSS 301: Quantitative Social Science Analysis and Big Data, and/or QMSS 451: Senior Capstone: Social Sciences Study Management. 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.

QMSS 451 includes training in data project management and data consulting techniques through unique partnerships with real organizations, units, and/or programs outside of QMSS. Students are guided and taught how to understand and define specific questions of the project, define and create scope of work documentation, and clean, analyze, and communicate data findings through agreed-upon deliverables using any combination of analytical tools as taught in QMSS core courses. Students get direct, hands-on experience applying quantitative skills through a social science lens as taught in QMSS core courses with the potential to have significant and positive impact for real organizations.

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 2 or 3 QMSS courses (QMSS 201, 301, and/or 451) each semester for a total of 5 courses per academic year. Courses are assigned by the QMSS Director and based on program needs and the lecturers expertise and technical skills. We are seeking applicants who are able to teach at least 2 of these 3 QMSS courses:

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.

QMSS 451: Senior Capstone: Social Sciences Study Management is a 3-credit seminar course (3 hours/week). Partnerships with external organizations, programs, and/or units will be made prior to the start of the semester. As a Lecturer III, this individual will work alongside QMSS program staff to identify, recruit, and build relationships with external organizations that will partner with QMSS 451 capstone courses. The lecturer also assists with assessing feasibility of projects based on data availability and the quantitative skills required, and they guide students as data consultants for the partner organizations to complete them. They will develop and deliver content related to project management, scope of work and other appropriate and necessary documentation for successful projects, and additional tools and skills as needed for individual projects. They serve as the liaison between students and the external organization throughout the semester and are responsible for properly maintaining all data and deliverables for the projects per responsible and ethical use guidelines and following other applicable processes and procedures.

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.

The Lecturer III will serve as a departmental advisor for undergraduate students minoring in QMSS, assisting them with course planning, progress checks, transfer credit evaluation, study abroad credit exceptions and other petitions, career/graduate school counseling, misconduct issues, and senior audits. They will work with the QMSS Director and Advisory Board to identify needs for the program and make recommendations for curricular/program innovation. The lecturer will also work with the staff in QMSS to plan program-wide and public events (e.g., guest speakers, panels, workshops, hackathons, etc.) geared towards undergraduate students minoring in and interested in QMSS.

Qualified candidates will have a PhD in a social science discipline with a demonstrated focus on quantitative methods prior to the start date of the position. 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.

Cutting-edge and contemporary knowledge and experience in applying data analysis and data science skills to social science-oriented research and/or teaching, with experience teaching, mentoring, and/or advising undergraduate students strongly preferred.

Preference will be given to versatile candidates who are social scientists with proficiency using a combination of Excel, R, and/or Python in all of the following: data visualization, cleaning, and communication for a wide range of audiences, data consulting and/or project management, web scraping and text-based analysis, geospatial analysis, and predictive analysis.

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 January 30, 2026 and we aim to schedule interviews in February. The offer to the final candidate is anticipated to be made before April 1.

The University of Michigan is an equal employment opportunity employer.