Sessional Lecturer - MMF1941H1F: Dynamic Data Science (Section LEC 0101)

University of Toronto; Toronto; Vor Ort; CAD 35.000 - 60.000; Vollzeit

Zusammenfassung

University of Toronto in Toronto (St. George campus) seeks a Sessional Lecturer for MMF1941H1F: Dynamic Data Science. Course runs Sept 14–Nov 30, 2026, with in-person Monday evenings. Responsibilities include lecture delivery, assessment, and student advising.

The ideal candidate holds an advanced degree in Mathematical Finance, with experience in time-series, streaming data, and university teaching of similar courses. CUPE unit 3 framework applies.

Qualifikationen

• Advanced degree in Mathematical Finance required or equivalent.
• Industry experience in data science techniques, time series and streaming data preferred.
• Prior experience teaching this course or similar at university level strongly desired.
• Ability to handle large classes and deliver clear lectures.

Aufgaben

• Prepare and deliver lectures for Dynamic Data Science (MMF1941H1F).
• supervise and grade tests and examinations in accordance with university regulations.
• Provide scheduled office hours for academic counseling of students.

Kenntnisse

Time series analysis

Streaming data

Large class teaching

Course development

Ausbildung

Advanced degree in Mathematical Finance

Jobbeschreibung

Sessional Lecturer – MMF1941H1F: Dynamic Data Science (Section LEC 0101)

Date Posted: 07/21/2026

Req ID: 49470

Faculty/Division: Faculty of Arts & Science

Department: Dept of Economics

Campus: St. George (Downtown Toronto)

Existing Vacancy: Yes

Course Number and Title: MMF1941H1F: Dynamic Data Science (Section LEC 0101)

Course Description: This course explores advanced data science techniques applied to dynamic, time-series, and streaming data, with a specific focus on financial markets and systems. Students will engage with predictive modeling, machine learning algorithms, and statistical analysis tailored for environments where data is continuously evolving.

Class Schedule: Monday 4:00-7:00 pm. Delivery method: in-person.

Sessional dates of appointment: September 14 – November 30, 2026.

Qualifications

• Advanced degree in Mathematical Finance
• Industry experience in data science techniques, time series and streaming data
• Prior experience teaching this course (or a similar course) at the university level
• Ability and experience teaching large classes

Preferred Qualifications

• Industry experience in predictive modeling, machine learning algorithms, and statistical analysis

Responsibilities

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

Contact: [e-mail]

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

Collective Agreement Information: 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.

Diversity Statement

The University of Toronto embraces Diversity and is building a culture of belonging that increases our capacity to effectively address and serve the interests of our global community. We strongly encourage applications from Indigenous Peoples, Black and racialized persons, women, persons with disabilities, and people of diverse sexual and gender identities. We value applicants who have demonstrated a commitment to equity, diversity and inclusion and recognize that diverse perspectives, experiences, and expertise are essential to strengthening our academic mission. As part of your application, you will be asked to complete a brief Diversity Survey. This survey is voluntary. Any information directly related to you is confidential and cannot be accessed by search committees or human resources staff. Results will be aggregated for institutional planning purposes. For more information, please see http://uoft.me/UP.

Accessibility Statement

The University strives to be an equitable and inclusive community, and proactively seeks to increase diversity among its community members. Our values regarding equity and diversity are linked with our unwavering commitment to excellence in the pursuit of our academic mission. The University is committed to the principles of the Accessibility for Ontarians with Disabilities Act (AODA). As such, we strive to make our recruitment, assessment and selection processes as accessible as possible and provide accommodations as required for applicants with disabilities. If you require any accommodations at any point during the application and hiring process, please contact [e-mail].