Course Lecturer (MGSC 696 076)

McGill University; Montreal; On-site; CAD 5,300 - 6,500; Part time

Job summary

McGill University invites applications for an instructional position to teach Advanced Topics in Management Science 2: Optimization for Data Science (MGSC 696 076). The course runs Tuesdays & Thursdays; location is to be announced, with a March 8, 2027 start and a April 30, 2027 end date.

Candidates should hold a PhD or Master’s in a quantified field and have university teaching experience in optimization, mathematical programming, machine learning, or data science.

Qualifications

• PhD (or MSc) in a quantitative field such as OR, MS, math, CS or data science.
• Relevant university-level teaching experience in the field.
• Ability to work in Canada/Quebec and communicate in English.

Responsibilities

• Teach the course Advanced Topics in Management Science 2, topic: Optimization for Data Science (MGSC 696 076).
• Deliver, assess and update course content aligned with data science optimization.
• Collaborate with the Desautels Faculty of Management - Operations Management Area and the academic affairs office.

Skills

Teaching experience

Optimization

Mathematical programming

Machine learning

Data science

Education

PhD or Master’s in Operations Research / Management Science / Applied Mathematics / Computer Science / Data Science

Job description

Advanced Topics in Management Science 2 For Topic: Optimization for Data Science MGSC 696 076 Tuesdays & Thursdays 19h35 - 20h55 Start Date: March 8, 2027 End Date: April 30, 2027 CL Credits: 1.5 Number of Class Hours: 19.5 Remuneration: $5,912.00 Deadline to apply: October 20 at 11:59 pm

Applications will be ranked according to the collective agreement between McGill University and the MCLIU. Preference will be given to the qualified applicant with the most priority points.

Teaching Qualification Requirements

• Education: PhD (or its equivalent), or Master's degree in Operations Research, Management Science, Applied Mathematics, Computer Science, Data Science, or a related quantitative field.
• Experience: Relevant teaching and/or industry experience in optimization, mathematical programming, machine learning, or data science, or related research publications in these areas.
• Other: University teaching experience.

Location TBA For any questions, please contact [e-mail]

Before applying, please note that to work at McGill University, you must be both authorized to work in Canada and willing to work in the province of Quebec at the campus where the position is based / located.

McGill University is an English-language university where most teaching and research activities are conducted in the English language, thereby requiring English communication both verbally and in writing.

Hiring Unit: Desautels Faculty of Management - Operations Management Area

Course Title and Logistics

Course Title: Advanced Topics in Management Science 2 For Topic: Optimization for Data Science Subject Code: MGSC 696 076 Location: ARMST 265 Schedule: Tuesdays & Thursdays 19h35 - 20h55 Deadline to Apply: 2026-10-20

McGill University hires on the basis of merit and is strongly committed to equity and diversity within its community. We welcome applications from racialized persons/visible minorities, women, Indigenous persons, persons with disabilities, ethnic minorities, and persons of minority sexual orientations and gender identities, as well as from all qualified candidates with the skills and knowledge to productively engage with diverse communities.

McGill implements an employment equity program and encourages members of designated groups to self-identify.

Persons with disabilities who anticipate needing accommodations for any part of the application process may contact, in confidence, [e-mail].

Located in one of the world's great multicultural and multilingual cities, McGill University is internationally recognized for its excellence as a leading institution of higher education and research. For nearly 200 years, through the work of dedicated people, McGill has been breaking ground in diverse fields and contributing solutions to some of the world's most significant issues. McGillians are proud to be part of a community that is both global and local, inspired by challenge and committed to shaping a better future. McGill received a Platinum STARS rating in sustainability, is among Canada's greenest employers, and is a top Montreal Employer.

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