TA335975- Fall 2025-CIS*4020 Data Science

Teaching Assistant Work Assignment
Posting Details
Type of Work Assignment and Bargaining Unit: 
GTA, Unit 1
Academic Unit: 
School of Computer Science
Semester(s) of Assignment(s): 
Fall 2025
This Work Assignment(s) may be assigned to fulfill the terms and conditions of a guaranteed Job Security Period: 
Yes
Course Details
Course Number: 
CIS*4020
Course Name: 
Data Science
Course Format: 
In-Class
Course Description: 
See Course Calendar
Other Course Description or Assignment Information: 
The total number of assignments is a low estimate of the number of available appointments. The final number may be higher and it will depend on class enrolment at the time of assignment.
Projected Class Enrolment: 
80
Level of Appointment for GTA, GSA-1 UTA(s)
The following is an estimate of number and levels of available appointments at the time of posting. This may change (increase or decrease) at the time of assignment contingent on departmental needs.
This Posting includes Multiple Work Assignments: 
No
Number of Available 0.25 (35 hour) Work Assignments: 
0
Number of Available 0.5 (70 hour) Work Assignments: 
1
Number of Available 0.75 (105 hour) Work Assignments: 
0
Number of Available Full 1.0 (140 hour) Work Assignments: 
0
Total Number (Load) of Assignment(s) Available: 
1
Duties and Responsibilities
Anticipated Duties and Responsibilities: 
Orientation-Training
Office Hours
Preparation
Conducting Labs/Seminars
Meetings
Invigilating Exams
Grading
Qualifications
Required Qualifications: 
• As per the Collective Agreement, must be enrolled as a student in the Fall 2025 semester. • The ability to communicate computer science concepts to students effectively, both in written form and verbally. • Must be available to invigilate in-class midterm and final exams. • Excellent problem-solving skills. • Demonstrated knowledge of data management, data visualization, data analysis, and data interpretation. • Demonstrated skills writing Python or R scripts.
Preferred Qualifications: 
• Familiarity with clustering algorithms, supervised or unsupervised matching learning algorithms, or other advanced data science methods. • Previous statistical analysis experience (such as regression, generalized linear models, or others). • Successful completion (>70% average) of STAT3240, STAT3510, STAT4000, STAT4350, STAT4360 or other related courses. • Successfully completed STAT*2040 (or similar introductory statistics course) with at least an 80% average
Days Required and Wages
Days and Times Required: 
See duties and qualifications above, as well as WebAdvisor for lecture and lab days and times.
Period of the Work Assignment (Start Date and End Date): 
September 2, 2025 to December 19, 2025
Wage Rate (per hour, per semester, per Full-load 1.0 Work Assignment): 
GTA $49.43/hr; $6,924.98/semester effective 2025/26
Other Posting Information
Application Deadline (All postings will automatically expire at 11:59 pm on this day): 
Sunday, July 6, 2025
Posting Email Contact: 
socspost@uoguelph.ca
Hiring Contact Information: 
Lauren King, Instructional Support Coordinator - teachingsupport@socs.uoguelph.ca

At the University of Guelph, fostering a culture of inclusion is an institutional imperative. The University invites and encourages applications from all qualified individuals, including from groups that are traditionally underrepresented in employment, who may contribute to further diversification of our Institution. For more information, the Office of Diversity and Human Rights (DHR) is a welcoming, safe and confidential one-stop shop for information, training and support on issues relating to diversity and human rights on our campus.

All applicants must be eligible to work in Canada specifically at the University of Guelph before applying for an academic work assignment. All successful applicants must perform their work in Ontario and must be able to attend on-campus in-person meetings as required.

Teaching Assistant work assignments are unionized with CUPE 3913 and their terms and conditions of work are covered by the Unit 1 Collective Agreement between the University and CUPE 3913 (email contact: president@cupe3913.on.ca).

The use of AI to perform job responsibilities must be directed by or approved beforehand in writing by the supervisor or course instructor.