• Analyze product usage data to identify behavioral patterns, segmentation opportunities, and growth drivers • Develop predictive models to analyze customer churn, retention, expansion, and product adoption • Build customer health scores and predictive signals to support proactive customer success strategies • Apply appropriate AI/ML techniques (e.g., advanced segmentation, pattern detection, NLP where relevant) to enhance insights • Translate business questions into analytical frameworks and data science solutions • Partner with Customer Success and Product teams to identify opportunities to improve engagement and reduce churn • Generate actionable insights and recommendations to influence business decisions • Communicate complex analyses clearly to both technical and non-technical stakeholders • Collaborate with BI teams to embed predictive insights into dashboards and reporting

Role Description MSC1123H F (0.25 FCE) An Introduction to Categorical Data Analysis. Categorical data is a type of qualitative data that involves grouping observations into categories instead of measuring them numerically. It is a common form of data found in many branches of science, so the ability to work effectively with categorical data is an important skill for researchers to possess. This course will provide students with the knowledge and skills to perform and interpret categorical data analysis involving: - Contingency tables - Logistic regression models The statistical software package R with the R Studio interface will be used. Both descriptive and inferential statistical methods will be covered. Estimated course enrolment: 25 Estimated TA support: none Class schedule: Online Sessional dates of appointment: September 1, 2026 to December 31, 2026 Salary (In accordance with the CUPE 3902 Unit 3 Collective Agreement): - Sessional Lecturer I - $4,998.74 - Sessional Lecturer I – Long Term - $5,349.61 - Sessional Lecturer II - $5,349.61 - Sessional Lecturer II – Long Term - $5,476.98 - Sessional Lecturer III - $5,476.98 - Sessional Lecturer III – Long Term - $5,614.45 Qualifications - Master’s or PhD degree in Statistics/Biostatistics or related discipline - Solid understanding of generalized linear models and categorical data analysis - Demonstrated effective teaching experience in a college or university setting - Preference for applicants with previous experience teaching categorical data analysis - Prior experience teaching graduate students is preferred Requirements - Preparation and delivery of course content - Development, administration, and marking of assignments, tests, and exams - Calculation and submission of grades - Holding regular office hours Application Procedure Please email your CV and CUPE 3902 Unit 3 application form with “MSC1123H Fall 2026 Sessional Lecturer Application” in the subject line to Sobiga Vyravanathan at cc.medscience@utoronto.ca by June 8, 2026. Closing Date 06/08/2026, 11:59PM EDT 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.

Role Description Responsible for analyzing data, uncovering the underlying data patterns and logic, and developing data-driven applications. They will work towards developing solutions for the entire problem-solving cycle: - Find/prioritize the problems - Research the best algorithms to solve the problems - Design robust, practical solvers - Implement them Does this position require Patient Care? No Essential Functions - Analyze complex and high-dimensional clinical and operational data for dependencies, patterns, outliers, inaccuracies, and validity. - Apply knowledge of statistics, machine learning, programming, data modeling, simulation, and/or advanced mathematics to recognize patterns, identify opportunities, pose business questions, and make valuable discoveries. - Contribute to the design and evaluation of optimal data-driven metrics (such as physician/facility performance criteria, bottleneck metrics, productivity limits, processing delay/error reports, etc.). - Use a flexible, analytical approach to design, develop, and evaluate predictive models and advanced algorithms that lead to optimal value extraction from the data. - Generate and test hypotheses and analyze and interpret the results. Qualifications - Bachelor's Degree Related Field of Study required or Master's Degree Related Field of Study preferred - Experience working in data science-type positions and with large data sets 2-3 years preferred - Ability to create reports and dashboards with Tableau. - Skilled in data analysis with Python and SQL. - Knowledge of statistics. - Knowledge of machine learning. - Preferred SQL database management and administrative knowledge. - Practiced knowledge of numerical optimization algorithms. Requirements - Remote Type: Remote - Work Location: 75 Francis Street - Scheduled Weekly Hours: 0 - Employee Type: Per Diem - Work Shift: Day (United States of America) - Pay Range: $36.19 - $52.67/Hourly - Grade: 6 Benefits - Competitive base pay - Comprehensive benefits - Career advancement opportunities - Differentials, premiums, and bonuses as applicable - Recognition programs designed to celebrate your contributions and support your professional growth

• To partner with stakeholders to identify, scope, and execute advanced analytics and data science projects • To design data modeling processes, create algorithms, and predictive models • To share insights that inform and enable business decisions • Builds, prototypes, and deploys machine learning models in production environments • Collaborates with product, technology, and other stakeholders to identify and scope data science projects • Communicates modeling challenges, findings, and outcomes to technical and non-technical stakeholders • Designs and implements end-to-end ETL (extract, transform, load) data pipelines integrating data from multiple data sources • Explores new internal and external data sources and assesses their commercial benefit • Mentors team members and serves as a subject matter expert in cross-functional discussions