Joining Data Course Staff

Apply for Data Course Staff for Spring 2025

Applications for Data course staff (TA/GSI, UCS2, UCS1, Reader) for Spring 2025 are now open!

To apply for an Academic Student Employee position for a Data course, please submit BOTH forms below:

1. ASE Application (required)
2. ASE Supplemental Application for Data Courses (required)

The priority deadline to apply for Spring 2025 is Friday, 10/18/2024, 11:59 PM.

While applications may be submitted after 10/18/2024 your best chance of being considered is to submit both application forms by the priority deadline.

Did you miss our faculty info session? View the session slides. 

Data Course Staff Overview

The College of Computing, Data Science, and Society appoints graduate and undergraduate students to support its instructional programs. Our outstanding staff teams bear significant responsibility for our students’ experience and learning in Data classes. Staff team members also form strong bonds with each other, mentor junior members, and create staff networks for academic and professional development.

Course staff positions are Reader, Undergraduate Course Staff 1 (UCS1), Undergraduate Course Staff 2 (UCS2), and Teaching Assistant (TA).

Academic Interns

Note: Academic Interns are not Academic Student Employees (ASEs). 

The Academic Internship program is not active for Data classes in Fall 2024 or Spring 2025.

Academic Internship is a common starting point for staff in Data 8 and Data 100. Academic Intern positions require enrollment in a P/NP class, typically for 1 unit. Correspondingly, the expected time commitment is 3 hours per week on average. AIs do not receive financial compensation. Typical responsibilities consist of providing support for students in discussion sections, labs, or office hours, supervised by TAs/UCS2s. The primary selection criteria are motivation for teaching and overall academic performance including performance in the class. 

All the other course staff positions are Academic Student Employee (ASE) positions for which there is financial compensation. The table below provides a brief description of the typical responsibilities as well as campus requirements and salary scales.

Please note:

• Detailed responsibilities for each position depend on the course and its instructors.
• The number of AI and ASE positions in a course can vary across semesters depending on the budget, course enrollment, and course structure.
• Undergraduate applicants who have no prior teaching experience are encouraged to consider AI, Reader, or UCS1 positions before applying to be UCS2s.

What positions are available?

Typical Timeline and Process

• ASE applications for the Summer and Fall are released in March or April. ASE applications for the Spring are released in October. Selection starts after the priority deadline for submission, but applications remain open for several weeks after that.
• Selection for ASE positions is a collaborative effort by the Faculty Director of Pedagogy, the course instructors, and relevant DSUS staff. Selection of AIs is a collaboration between the Faculty Director of Pedagogy, the course instructors, and some senior course staff.
• Offers are made starting shortly after the priority deadline. The process can continue till the start of the semester of appointment, for example due to changes in appointees’ own plans.

Some Common Pathways

Berkeley offers a uniquely innovative set of undergraduate Data Science courses. 

Applicants for Teaching Assistant (TA) positions who have neither taken nor taught the course for which they are applying should describe the background that qualifies them for the position. Qualified graduate students have priority over undergraduates.

Teaching is a skill that develops over years of experience. All Data course staff participate in regular meetings to discuss pedagogy and best practices. Undergraduate staff typically start as UCS1s and later apply for positions of greater responsibility. The typical progression is UCS1, then UCS2, with at least one semester as UCS1.

UCS2 Partial Fee Remission Information

Data Science UCS2s (Undergraduate Course Staff 2s) are eligible for a partial fee remission based on the appointment percentage of the value of the full fee remission guaranteed under Article 11 - Fee Remission of the UAW 2865 Collective Bargaining Agreement.

UCS2 Fee Remission Structure