Department of Computer Science

Faculty of Science

Job details:

Course: Data Science with Real World Data Sets DATA 3010 B01

Start date

September 2, 2026

End date

January 8, 2027

Work schedule

2 hours per week

Expected total hours

26 hours

Hourly rate

$21.82 per hour (plus vacation pay) for undergraduate students $24.29 per hour (plus vacation pay) for graduate students

Hiring 1 TA/Lab demonstrator for DATA 3010 B01 W 1230 to 1320. __ TAs for ""B"" sections lead the lab tutorials and must be available to facilitate all sessions. TAs may be asked to support the course instructor as needed throughout the term. __ Weekly paid hours relate to scheduled class/lab hours and 1 hour prep per course. TAs are not scheduled or paid over the Fall term break, unless they are supporting the instructor with course tasks like grading. __ Expected work dates for TAs begin the week before classes start to the end of the exam period.

The week prior to classes, there will be an in-person TA orientation and instructors may hold course meetings. During the exam period, TAs might be asked to invigilate final exams or support with grading for their assigned course. __ Previous experience teaching, tutoring, or leading is preferred. Great understanding of the challenges programming students face is required.

Good knowledge of 3000-level and 4000-level CS courses and programming languages is preferred. An A in the course or demonstrated subject expertise is preferred. __ Course Specific Requirements: This course is cross-listed with COMP 3360 and TA should have some familiarity with that course. Familiarity with Python, particularly the PyTorch, NumPy, Pandas, Matplotlib libraries.

Familiarity with developing and running Python programs with Visual Studio Code. Familiarity with the fundamental concepts of the following topics and able to clearly explain their implementation and demonstrate them using Python: linear regression, linear classification (softmax regression), optimization in deep learning (mini-batch stochastic gradient descent, momentum, Adagrad, RMSProp, and Adam), multilayer perceptron, batch normalization, convolutional neural networks, LeNet, recurrent neural networks,



and transformers. Ability to develop interactive Jupyter notebooks for explaining Python code examples.

Fundamental understanding of data manipulation and preprocessing using Python. Understanding of basic concepts of object-oriented programming. __ INDICATE YOUR AVAILABILITY AND ANY COURSE/SECTION PREFERENCES IN YOUR COVER LETTER.

For more information please contact: Jessica Watson - [email protected]

Key responsibilities:

- Attends orientation, planning and coordinating meetings as may be scheduled for staff in the course.
- May be required to attend lectures and other sessions of instruction in the course.
- Consults with the employment supervisor responsible for the course(s) for direction on assigned responsibilities.
- Prepares instructional material such as handouts, assignments, problem sets, tests, exams and presents to students in a variety of settings such as tutorials, laboratories, or seminars.
- Marks student work including the work submitted by students in the tutorial, lab or seminar for which she/he is responsible and the assigned portion of the work submitted by students in the course generally, e.g. the midterm exam, final exam or major project, the marking of which may be shared among the staff in a course under the employment supervisor responsible.
- Consults with students by maintaining regularly scheduled and posted times for such consultation and provides a reasonable amount of informally scheduled consultation if necessary.
- Such other related duties as may be assigned, e.g. development or adaptation of audio visual material, preparation of experiments, participation in field trips, etc.
- - Occasional approved substitution for other members of the teaching or teaching support staff including the necessary related tasks.





- - May initiate or be required to initiate information to students identifying assignment problems or misinterpretations.

If you are an international student you must have a valid study permit which states that you are allowed to work on campus and you must be enrolled as a full time University of Manitoba student for the duration of any appointment. Applications may be considered after the posting closing date.

Qualifications:

- Completion of academic studies and experience which have resulted in an expertise specifically appropriate to assisting in the instruction of the course(s) assigned.
- An empathetic approach to the instructional needs and concerns of students.
- Effective skills in the English language.

Preference may be given to applicants who are, or will be, a registered student studying in the Department which is posting the employment opportunity at the time that the duties of the position commence. PLEASE ATTACH A COPY OF YOUR LATEST STUDENT TRANSCRIPT. PAST ACADEMIC PERFORMANCE MAY BE A SIGNIFICANT FACTOR IN SELECTION DECISIONS.

Additional information:

The University of Manitoba is committed to the principles of equity, diversity & inclusion and to promoting opportunities in hiring, promotion and tenure (where applicable) for systemically marginalized groups who have been excluded from full participation at the University and the larger community including Indigenous Peoples, women, racialized persons, persons with disabilities and those who identify as 2SLGBTQIA+ (Two Spirit, lesbian, gay, bisexual, trans, questioning, intersex, asexual and other diverse sexual identities). If you require accommodation supports during the recruitment process, please contact [email protected] or (phone hidden). Please note this contact information is for accommodation reasons only.

Application materials, including letters of reference, will be handled in accordance with the protection of privacy provisions of "The Freedom of Information and Protection of Privacy Act" (Manitoba). Please note that curriculum vitae will be provided to participating members of the search process.

📌 TA/Demo/TUR/Sem. Leaders - DATA 3010 (CUPE Students) (Winnipeg)
🏢 University of Manitoba
📍 Winnipeg