Canadian Tire Corporation, Limited ("CTC") is one of Canada's most admired and trusted companies. With more than 90 Owned Brands, 1,700 retail locations, financial services, exemplary e-commerce capabilities, and exciting market-leading merchandisin
Responsibilities
You will lead high-impact statistical modelling work that helps shape decisions across retail, customer, and loyalty. This role is ideal for someone who enjoys solving complex business problems with rigorous quantitative methods and translating that work into real business action.
In this role, you will:
Lead the design and development of advanced statistical and quantitative models using retail, customer, and loyalty data
Apply methods such as forecasting, causal inference, experimentation, predictive modelling, segmentation, optimization, and statistical learning to solve important business problems
Partner closely with business leaders to identify where modelling can most improve decision-making, customer outcomes, and financial performance
Build models and analytical frameworks that are practical, scalable, and decision-oriented
Establish strong standards for model design, validation, monitoring, and measurement
Translate complex quantitative work into clear recommendations for senior leaders
Help shape the future of modelling and experimentation within the broader analytics organization
Coach and develop a team of analysts and data scientists, while remaining close to the work and setting a high bar for technical quality
Why this role is interesting
This is not a generic data science role. It is an opportunity to work on meaningful modelling problems with direct impact on:
Customer growth and retention
Loyalty performance
Promotional effectiveness
Forecasting and demand signals
Measurement of business initiatives
Strategic retail decision-making
You'll have the chance to work with large, complex datasets and influence decisions that matter across the enterprise.
Requirements
You are a strong statistical thinker who enjoys applying quantitative methods to real business problems. You are comfortable moving between technical depth and executive discussion, and you know how to build confidence in your work through rigor, clarity, and practical impact.
You likely bring:
Deep curiosity and strong problem-solving ability
A strong foundation in statistics, probability, and quantitative modelling
Experience working through ambiguity and turning open-ended questions into structured analytical approaches
The ability to balance hands-on technical work, coaching, and stakeholder influence
Strong judgment on when to use the right level of complexity for the problem
What you bring
Graduate degree in Statistics, Mathematics, Econometrics, Economics, Data Science, Computer Science , or another quantitative field
PhD is welcome, but not required
Significant experience in statistical modelling, advanced analytics, or quantitative decision support
Strong command of methods such as:
Regression and classification
Forecasting / time series
Experimental design and measurement
Causal inference
Statistical learning
Model validation and performance assessment
Strong coding experience in Python
Experience working with large datasets using SQL , Spark, or similar tools
Experience in modern data environments such as Databricks, cloud platforms, or distributed computing frameworks
Strong communication skills, with the ability to explain technical work clearly to non-technical audiences
Experience leading projects, influencing senior stakeholders, and helping others raise the quality of their work
Experience in retail, loyalty, consumer, pricing, marketing, or customer analytics
Experience with experimentation platforms , uplift modelling, optimization, or decision science
Experience building modelling workflows that move beyond analysis into operational use
We're always looking for great talent! In addition to competitive pay, we offer:
This posting represents an existing vacancy within our organization.
We may use artificial intelligence tools as part of our recruitment process to assist in the initial screening of resumes. All hiring decisions, including candidate evaluation, selection, and disposition, are made by human recruiters.
Canadiantirecorporation · Toronto, On, Canada