Description

The School of Data Science and Society (SDSS) at the University of North Carolina at Chapel Hill invites applications for a Teaching Assistant Professor position to support its growing academic programs. We seek candidates with a doctoral degree in data science or a related field and a strong commitment to excellence in pedagogy for data science. 
  
Given the interdisciplinary nature of the school and the broad interpretations of data science, we encourage applications from any substantive area that contributes to the mission, vision and values of SDSS. Candidates should also explain their methodologic expertise as it pertains to the domains of data science.  
 
Applicants having prior experience teaching college-level introductory classes on foundations of data science are especially encouraged to apply.  In addition to teaching, successful applicants will demonstrate the ability to advise and mentor the school’s growing undergraduate population.  
 
This position will be expected to dedicate 70-80% of time to teaching data science courses, 0-10% on pedagogical research, and 20% of the effort in service to the School. This position will be encouraged to participate in prioritized research activities on pedagogical techniques and technologies, data science academic trends and topics, and contribute to the school’s efforts to expand its data science course offerings, including a new AI minor which is in development for launch in Fall 2026. 
 
This position will be a 9-month appointment with the opportunity to teach in summer based on needs in the undergraduate program. The instructional load for this position will be 3 courses per semester for 6 courses taught each academic year. This is a permanent fixed-term position. The position will have an initial appointment of 3 years with the possibility of reappointment and a pathway for advancement in rank. The courses taught by the candidate may include a mix of introductory and advanced ones with a priority to cover the introductory level courses. Advanced courses will be selected from a range of thematic areas that match the faculty’s expertise. Teaching assistants will provide support to large-format courses through homework grading and tutorial sessions. Sample courses include DATA110: Introduction to Data Science, DATA 117: AI Unlocked for Curious Minds, DATA120: Ethics of AI and Societal Decision Making, DATA130: Critical Data Literacy, DATA 140: Introduction to Data Structures and Management, and DATA150: Communication for Data Scientists. Interest in the aforementioned data science courses is rapidly growing with engagement of students in a variety of disciplines including data science, business, economics, among others. 
 
More information about the BS in Data Science curriculum can be found on our website. Additionally, we are also seeking faculty for capstone courses for master’s students in data science. This involves working with partners across campus, in industry, and in the community to source and manage projects for the capstone course.  
 
Consideration of applications will begin on October 15, 2025, and we will continue accepting applications until the position closes on January 9, 2026. The positions will be effective July 1, 2026. 
 
Visit https://unc.peopleadmin.com/postings/307044 to apply.  All faculty candidates must apply online via UNC’s website and submit a cover letter and resume.  The “other document” in the list of required documents to be submitted is a teaching statement and a research statement, combined into one document. Please arrange for four reference letter writers to submit letters where indicated. They will receive an email with instructions for submitting letters of recommendation. Please ensure that at least one of the four letters addresses your teaching experience and qualifications. 
 
For inquiries, please contact sdss-hr@unc.edu. 
 
The University of North Carolina at Chapel Hill is an equal opportunity and affirmative action employer.  All qualified applicants will receive consideration for employment without regard to age, color, disability, gender, gender expression, gender identity, genetic information, national origin, race, religion, sex, sexual orientation, or status as a protected veteran.