Lecturer in Data and Intelligent Systems

About this position

Data and Intelligent Systems (DaIS) at Princeton University seeks to appoint outstanding lecturers to join our faculty in developing and teaching exciting undergraduate Data Science, Statistics, and Machine Learning courses. Depending on the applicant's background and DaIS’ needs, responsibilities may include a combination of teaching or co-teaching full courses, giving individual lectures, leading precepts, supervising graduate student Teaching Assistants, participating in course and curriculum design, grading Independent Work, and connecting research to courses. Full-time positions may be available. Any appointments and duty time are subject to enrollments and may be for one or two semesters during the 2026-2027 academic year. Positions would have an expected start date of September 1, 2026, or January 16, 2027. Appointments will be made at the rank of Lecturer and will be made through Data and Intelligent Systems. The work location for this position is in-person on campus at Princeton University. Elevating the individual within a culture of collaboration, Princeton University is a powerful foundation upon which to build one’s career and family. Princeton’s competitive array of benefits reflects a holistic approach to supporting individuals and their families at work and beyond. The University is also an engaging, transformative resource in and of itself. From opportunities for collaboration to world-class research and cultural facilities, you will find all manner of spaces that bring people together and inspire new ideas. DaIS is a new unit within Princeton University which will succeed the Center for Statistics and Machine Learning ( https://csml.princeton.edu/ ) as of the 2026-2027 academic year. Essential Qualifications: Advanced degree in computer science, statistics, or a related field. Ability to program in Python or R. Preferred Qualifications: Ph.D., prior teaching experience at the college or university level, and some background in applications to

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