LEO Lecturer I - QMSS 301 Fall 2024

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How to Apply

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. 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,
• a Diversity Statement: a document describing how your work would contribute to QMSSs, the College of LSAs and University of Michigan's strategic commitment to diversity, equity, and inclusion, and specifically to inclusive teaching practices and environments (https://sites.lsa.umich.edu/inclusive-teaching/),
• 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.
  

Summary

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. 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.

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 26, 2024. This is a non-tenure track position with an appointment period for the Fall 2024 term (i.e., August 26, 2024 to December 31, 2024).

QMSS is seeking part-time lecturers for either 33% or 67% appointment efforts to serve as section instructors of the laboratory sections for QMSS 301: : Quantitative Social Science Analysis and Big Data. A 33% appointment requires teaching 2 laboratory sections per week, and a 67% appointment requires teaching 4 laboratory sections per week, each under the guidance of a faculty lecturer who serves as the instructor of record for the course.

QMSS 301 includes 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.

Mission Statement

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.

Responsibilities*

The lecturer will teach either 2 (33% appointment) or 4 (67% appointment) laboratory sections of QMSS 301 with the guidance of a faculty lecturer as the instructor of record for the course.

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

• Attend course lectures (3 hours/week) and actively participate and assist students as needed by the instructor of record.
• Prepare lessons, curate data sets, and assist with weekly laboratory assignment creation as needed to effectively teach either 2 x 1-hour lab sections (33% appointment) or 4 x 1-hour lab sections (67% appointment) each week that reflects and applies material covered during lectures. 
• 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 either 7 (33% appointment) or 14 (67% appointment) 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 section instructors from each 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*

A master's degree in a social science discipline with a demonstrated focus on quantitative methods is required, with a Ph.D. strongly preferred. Experience teaching courses using quantitative methods and analysis, with demonstrated proficiency in teaching and/or using a coding-based statistical program is required.

Desired Qualifications*

Proficiency with multiple analytical tools and approaches, including R and/or Python, for use in big data techniques is preferred.

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.

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

Union Affiliation

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.

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

Questions about applying for this position can be emailed to: [email protected]

Application Deadline

Anticipated application deadline is July 6, 2024, 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 1, 2024.

U-M EEO/AA Statement

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