Course Lecturer (MGSC 696 075)
- Montreal, QC, Canada
- Bronfman Building, Canada
Remuneration: $5,912.00 · Part-time
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Job details
- Pay
- Remuneration: $5,912.00
- Job type
- Part-time
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- Posting language
- English
- Posted
Original job description
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Advanced Topics in Management Science 2
For Topic: Optimization for Data Science
MGSC 696 075
Tuesdays and Thursdays 18h05 - 19h25
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
The Desautels Faculty of Management is inviting applications to teach a course in Winter 2027. Please complete the online application form and upload required documents. 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 academicaffairs.mgmt@mcgill.ca
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 AreaCourse Title:
Advanced Topics in Management Science 2 For Topic: Optimization for Data ScienceSubject Code:
MGSC 696 075Location:
ARMST 260Schedule:
Wednesdays 14h35 - 17h25Deadline to Apply:
2026-10-20McGill 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, accessibilityrequest.hr@mcgill.ca.
More about this job
Responsibilities
Teach the course Advanced Topics in Management Science 2, focused on Optimization for Data Science, for the Desautels Faculty of Management. Deliver 19.5 class hours during the scheduled course period.
Requirements
Applicants must have a PhD or equivalent, or a master's degree in a relevant quantitative field such as Operations Research, Management Science, Applied Mathematics, Computer Science, or Data Science. Relevant teaching or industry experience, or research publications, in optimization, mathematical programming, machine learning, or data science is required, along with university teaching experience.
Skills
- Optimization
- Mathematical Programming
- Machine Learning
- Data Science
- Operations Research
- Management Science
- Applied Mathematics
- Computer Science
- University Teaching
Visa sponsorship
Not detected in the job text
Categories
- Education
- Data & Analytics
- Science & Research
Keywords
- Advanced Topics in Management Science
- Optimization for Data Science
- Operations Management
- Operations Research
- Management Science
- Applied Mathematics
- Computer Science
- Data Science
- Optimization
- Mathematical Programming
- Machine Learning
- University Teaching
- Teaching Experience
- Industry Experience
- Research Publications