Assistant Professor, Teaching Stream – Contractually Limited Term Appointment - Statistical Sciences

The Department of Statistical Sciences in the Faculty of Arts and Science at the University of Toronto invites applications for a contractually-limited term appointment (CLTA) in the field of Statistical Sciences.

The appointment will be at the rank of Assistant Professor, Teaching Stream for a one-year term with an anticipated start date of July 1, 2025.

This search aligns with the University’s commitment to strategically and proactively promote diversity among our community members ().

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.

Applicants are required to have earned a Ph.D. 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

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 .

Evidence of excellence in teaching and a commitment to 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.

Salary will be commensurate with qualifications and experience.

All qualified candidates are invited to apply online at Academic Jobs Online, 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 . 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 November 25, 2024.