Assistant Professor, Teaching Stream – Contractually Limited Term Appointment – Applied Statistics Contract NEW

The Department of Statistical Sciences in the Faculty of Arts and Science at the University of Toronto invites applications for a three-year full-time contractually-limited term appointment (CLTA) in the field of Applied Statistics. The appointment will be at the rank of Assistant Professor, Teaching Stream with an anticipated start date of August 1, 2026.

This search aligns with the University’s commitment to strategically and proactively promote diversity among our community members (Statement on Equity, Diversity & Excellence). Recognizing that Black, Indigenous, and other Racialized communities have experienced inequities that have developed historically and are ongoing, we strongly welcome and encourage applicants from those communities to apply.

Candidates must have earned a Ph.D. degree in Statistics, Biostatistics, Data Science or a related field by the time of appointment or shortly thereafter. Alternatively, applicants are required to have (i) a Masters in Statistics, Biostatistics, Data Science or related field with (ii) at least 12 months of teaching experience with excellent performance in a degree-granting program/post-secondary institution, and (iii) demonstrated excellent scholarly or creative professional activity in areas such as, but not limited to, exemplary teaching practices, development of pedagogical software tools, course or curriculum development, or engagement with Statistics, Biostatistics and/or Data Science Education research.

Applicants must have a minimum of one-year experience teaching a variety of university level, degree granting courses in Statistics, Biostatistics or Data Science that include computation using R, Python, or another programming language, including lecture preparation and delivery, curriculum development, and development of online material/lectures. The successful applicant should be prepared to teach advanced and introductory undergraduate statistics and data science courses to students with a range of mathematical and computational backgrounds. A full list of courses can be found at https://artsci.calendar.utoronto.ca/section/Statistical-Sciences.

Applicants must have a demonstrated record of excellence in teaching statistics, biostatistics or data science teaching, including lecture preparation and delivery of innovative course materials, activities and assessments with a demonstrated commitment to pedagogical growth. Experience teaching large classes is considered an asset. Additionally, applicants must possess a demonstrated commitment to excellent pedagogical inquiry and a demonstrated interest in teaching-related scholarly activities. We seek applicants whose teaching interests complement and strengthen our existing departmental strengths in Statistical Sciences.

Evidence of excellence in teaching and a commitment to excellent pedagogical inquiry can be demonstrated through teaching accomplishments, awards and accolades, presentations at significant conferences, the teaching dossier submitted as part of the application including a strong teaching statement, sample syllabi and course materials, and teaching evaluations, as well as strong letters of reference from referees of high standing.

The salary range is $105,000 to $150,000 CAD per annum, depending on experience. Negotiations beyond this salary range will be considered for exceptionally qualified candidates.

All qualified candidates are invited to apply online at Academic Jobs Online, https://academicjobsonline.org/ajo/jobs/31933 and must submit a cover letter; a current curriculum vitae; and a complete teaching dossier to include a teaching statement, sample syllabi and course materials, and teaching evaluations. Equity and diversity are essential to academic excellence as articulated in the University of Toronto’s Statement on Equity, Diversity and Excellence. We seek candidates who share these values and who demonstrate throughout the application materials their commitment and efforts to advance equity, diversity, inclusion and the promotion of a respectful and collegial learning and working environment.

Applicants must also arrange to have three recent letters of reference (on letterhead, dated and signed) uploaded through Academic Jobs Online directly by the writers by the closing date. At least one reference letter must primarily address the candidate’s teaching.

All application materials, including signed recent reference letters, must be received by May 15, 2026.

For more information about the Department of Statistical Sciences, please visit our website at https://www.statistics.utoronto.ca or contact Katrina Mintis at [email protected].

All qualified candidates are encouraged to apply; however, Canadians and permanent residents will be given priority.

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.

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 [email protected].