"Columbia University Adjunct Faculty"

The Quantitative Methods in the Social Sciences (QMSS) MA Program at Columbia University invites applications for adjunct faculty positions to teach courses in Spring 2026, in the broad areas of machine learning, data science, research methods, and data analysis; there is also the potential option for adjunct faculty to design new courses in their areas of expertise in the future.

Instructors who teach in the QMSS MA Program will be appointed by the academic department in the Faculty of Arts and Sciences relevant to the incumbent’s own field. Adjunct faculty teach at most one course each semester. Positive performance can lead to additional teaching opportunities. Faculty teach in-person on campus, typically in the evening, but times are negotiable.

QMSS provides substantial resources to ensure adjunct faculty are well-supported and the classroom experience is successful for everyone. QMSS is one of the oldest interdisciplinary applied statistics MA programs of its kind, with almost 25 years of training graduate students. We count hundreds of data scientists, senior analysts and academic researchers among our alumni. We continually strive to develop innovative approaches that push the boundaries of where social science and data science intersect at Columbia. Please join us!

Instructions to the Applicant:
All applications must be made through Columbia University’s Academic Search and Recruiting(ASR/Interfolio).
For more information and to apply, please go to:
https://academic.careers.columbia.edu/#!/171525?keywords=adjunct%20faculty&sortKey=keywordScore

Equal Employment Opportunity Statement
Columbia University is an Equal Opportunity Employer / Disability / Veteran

Pay Transparency Disclosure
The salary of the finalist selected for this role will be set based on a variety of factors, including but not limited to departmental budgets, qualifications, experience, education, licenses, specialty, and training. The above hiring range represents the University’s good faith and reasonable estimate of the range of possible compensation at the time of posting.