Apply for an Academic Student Employee position

Applications for Spring 2024 are open!

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

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

The priority deadline to apply for an ASE position is Friday, October 27 at 11:59 PM. 

While applications may be submitted after October 27, your best chance of being considered is to submit both application forms by the priority deadline.

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 Academic Intern (AI), 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). 

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?

Position

Typical Responsibilities

Courses 
(Spring 2024)

Campus Requirements

Salary

Reader

Primarily responsible for grading and office hours.

Data C104

Requirements for Readers

Salary for Readers

Undergraduate Course Staff 1 (UCS1)

Primarily responsible for academic support in small-group tutoring sessions.

Data C8, Data C88C, Data C88S, Data C100, Data C102, Data C140, Data 198

Requirements for UCS1s

Salary for UCS1s

Teaching Assistant (TA)/ Undergraduate Course Staff 2 (UCS2)

Teaches discussion and lab sections; collaborates on diverse aspects of course execution. 

Data C8, Data C88C, Data C88S, Data C100, Data C102, Data C104, Data C140, Data 198

Requirements for UCS2s

Requirements for TAs

Salary for TAs

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.

  • The application for Academic Interns for Fall semester is released in June or July, and the application for Spring semester is released in January.

  • 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 AIs or UCS1s and later apply for positions of greater responsibility. Here are some paths commonly taken by undergraduates on our staff teams.

  • Data 8 and Data 100: A common progression is AI, then UCS1, then UCS2, with at least one semester of each as AI and UCS1. 

  • Data 140: 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

Appointment Percentage Fee Remission Percentage
20% 40%
25% 50%
30% 60%
Above 30% 100%