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Infographic showing various Volunteer Lecturer In Computer Science job openings in Toronto, ON as of July 2026, with employment types broken down into 1% As Needed, 82% Full Time, 14% Part Time, and 3% Contract. Highlights an 90% Physical, 5% Hybrid, and 5% Remote job distribution.
Sessional Lecturer - MMF1922H1F: Data Science (Section LEC 0101)

Sessional Lecturer - MMF1922H1F: Data Science (Section LEC 0101)

University of Toronto

Toronto, ON

CA$4.9K/wk

Full-time

Posted 4 days ago

Job description

Date Posted: 07/21/2026
Req ID: 49466
Faculty/Division: Faculty of Arts & Science
Department: Dept of Economics
Campus: St. George (Downtown Toronto)
Existing Vacancy: Yes

Description:

Course Number and Title: 

MMF1922H1F: Data Science (Section LEC 0101)

Course Description:
Over the past decade, data science and machine learning have gained immense popularity in many scientific disciplines. The reason for the emergence is due to theoretical advances in machine learning, availability of big data, and surges in computational capabilities. This 8-week course provides an introductory overview of data science methods in finance, investments, and risk management. The course covers a review of foundational probability and statistics, brief introduction to machine learning (supervised learning, unsupervised learning) and big data tools.

Estimated course enrolment: 30

Estimated TA support: n/a

Class Schedule                                       Class Schedule: Thursday 6:00-9:00 pm

The delivery method for this course is in-person.

Sessional dates of appointment: September 10 - October 29, 2026

Salary (per section):

$4,998.74 Sessional Lecturer I

$5,349.61 Sessional Lecturer I - Long Term

$5,349.61 Sessional Lecturer II

$5,476.98 Sessional Lecturer II - Long Term

$5,476.98 Sessional Lecturer III

$5,614.45 Sessional Lecturer III - Long Term

 

Please note that should rates stipulated in the collective agreement vary from rates stated in this posting, the rates stated in the collective agreement shall prevail.

Minimum qualifications:

  • Advanced degree in Mathematical Finance
  • Industry experience in data science and machine learning methods in finance
  • Prior experience teaching this course (or a similar course) at the university level
  • Ability and experience teaching large classes

Preferred qualifications:

  • Industry experience in big data and machine learning in finance

Description of duties:

  • Preparation and delivery of lectures in this course
  • Preparation, supervision and grading of tests and examinations in accordance with university regulations
  • Providing scheduled office hours for academic counseling of students

Application instructions:  

Applicants should submit an updated curriculum vitae; names and contact information (email and phone) for two referees or two reference letters; evidence of teaching in the relevant area, including student evaluations if available; and the CUPE 3902 Unit 3 application form located here: https://www.economics.utoronto.ca/index.php/index/recruiting/sessionalOpeningsForm.

Please attach the additional documents in one PDF file format to the application form. If you have any questions, please contact sessional.economics@utoronto.ca All applicants must have a valid email address.

Closing Date: 08/14/2026, 11:59PM EDT
**

This job is posted in accordance with the CUPE 3902 Unit 3 Collective Agreement. 

 It is understood that some announcements of vacancies are tentative, pending final course determinations and enrolment. Should rates stipulated in the collective agreement vary from rates stated in this posting, the rates stated in the collective agreement shall prevail.  

Preference in hiring is given to qualified individuals advanced to the rank of Sessional Lecturer II or Sessional Lecturer III in accordance with Article 14:12 of the CUPE 3902 Unit 3 collective agreement.

Please note: Undergraduate or graduate students and postdoctoral fellows of the University of Toronto are covered by the CUPE 3902 Unit 1 collective agreement rather than the Unit 3 collective agreement, and should not apply for positions posted under the Unit 3 collective agreement.

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