Lecturer - Data Science Undergraduate Studies - College of Computing, Data Science, and Society

Overview

Position Overview

Data Science Undergraduate Studies (DSUS) at the University of California, Berkeley invites applications for a pool of qualified temporary lecturers to teach DSUS courses should an opening arise. Screening of applicants is ongoing and will continue as needed. The number of positions varies from semester to semester (fall, spring and summer sessions), depending on the needs of the unit.

Salary range: The UC academic salary scales set the minimum pay  the following table for the current salary scale for this position: https://(Use the "Apply for this Job" box below). The current full-time salary range for this position is $70,977-$199,722. Placement on the scale is commensurate with college teaching experience.

Percent time: 15% to 100%

Anticipated start: Positions usually start in July or August for Fall, January for Spring and June for Summer.

Review timeline: Applications will be accepted and reviewed for unit needs through January 2027. Applications are typically considered in April and May for fall course needs, in September and October for spring course needs, and February and March for summer course needs. The pool will close January 2027; applicants wishing to remain in the pool after that time will need to submit a new application.

DSUS is seeking outstanding instructors to be appointed in the non-Senate Lecturer title series who can teach small and large courses in several areas. We are particularly interested in instructors who can combine computational and inferential thinking in a way that reflects the new field of Data Science Education.

Data Science Undergraduate Studies (DSUS) offers a range of academic, co-curricular, and enrichment programs—including the Data Science major and minor—with a wide-reaching impact both across UC Berkeley and beyond. Designed in collaboration with faculty from across Berkeley, Data Science invests students with deep technical knowledge, expertise in how to apply that knowledge in a field of their choosing, and an understanding of the social and human contexts and ethical implications of how data are collected, analyzed, and used.

This combination positions graduates to help inform and develop solutions to a range of pressing challenges, from adapting industry to a new world of data to amplifying learning in education to helping communities recover from disaster.

DSUS is part of the College of Computing, Data Science, and Society (CDSS), which strives to develop, implement, and share high-quality, ethics-oriented, and accessible curricula, educating a diverse student body in data science, computing, and statistics. Core to the college is an understanding of how computing and data science affect equality, equity, and opportunity—and the capacity to respond to social challenges.

DSUS is committed to hiring and developing staff who want to work in a high performing culture that reflects the outstanding work of our faculty and students. DSUS seeks candidates who can support the success of all students through inclusive curriculum, classroom environment, and pedagogy.

DSUS is seeking outstanding instructors to be appointed in the non-Senate Lecturer title series who can teach small and large courses in several areas. We are particularly interested in instructors who can combine computational and inferential thinking in a way that reflects the new field of Data Science Education.

• Fundamentals of Data Science
• Principle and Techniques of Data Science
• Human Contexts and Ethics of Data
• Data and Justice
• Data, Inference, and Decisions
• Honors Thesis Seminar

Instructors may be hired to teach Connector Courses that connect Foundations of Data Science with other disciplines, such as neuroscience, legal studies, public health, demography, English or others. Connector courses allow students to apply theoretical concepts from data science to a particular area of interest. Course design and syllabus will leverage the sequence of computational and statistical techniques that students learn in the…