AVP Data Science - Canadian Tire, Toronto

AI Summary

Canadian Tire is seeking an AVP, Data Science in Toronto to provide hands-on technical leadership for advanced analytics and AI across the organization. This senior role involves leading the design, development, and productionization of large-scale, enterprise-grade data science and AI solutions.

Key duties include owning company-wide data science and AI engineering standards and modernizing the software development lifecycle through AI-native development practices. Top requirements include over 15 years of experience in data science, machine learning, or applied AI, with deep hands-on expertise in modern data science and ML development, and a proven ability to lead technically across multiple teams.

About the Role

As the AVP, Data Science at Canadian Tire, you will serve as a pivotal technical leader, driving the evolution of advanced analytics and artificial intelligence capabilities throughout the enterprise. This is a significant opportunity to lead the design, development, and productionization of sophisticated, scalable data science and AI solutions that will operate in real-world production environments. Your expertise will be crucial in setting the technical direction for how data science is practised at scale, ensuring high-quality outputs and efficient delivery across various teams. You will also be instrumental in modernizing the software development lifecycle (SDLC) by championing AI-native development practices. This includes the strategic adoption of cutting-edge AI-assisted coding tools, such as OpenAI Codex and Anthropic Claude, to materially enhance developer productivity, code quality, and overall project delivery speed. This commitment to innovation ensures that Canadian Tire remains at the forefront of technological advancement in the retail sector, leveraging data science to create impactful business solutions.

Key Responsibilities and Impact

This role demands a senior technical authority who can lead hands-on architecture, modelling, and implementation of complex, production-grade machine learning and AI solutions. You will own and modernize the data science SDLC end-to-end, establishing AI-native development workflows that improve throughput, code quality, testing, and documentation. A core part of your mandate will be to define, enforce, and evolve company-wide data science and AI engineering standards, encompassing coding standards, model development practices, MLOps patterns, and peer review expectations. Furthermore, you will build and lead the Data Science Community of Practice across the organization, setting technical direction, facilitating knowledge sharing, and mentoring senior data scientists. This leadership will establish consistent quality bars for methods, code, and outputs, driving consistency, reuse, and efficiency across teams through reusable libraries, reference architectures, and shared tooling. You will also partner closely with Data Engineering, platform teams, and business leaders to ensure that data science solutions are not only production-ready and scalable but also deeply aligned with critical business priorities, influencing roadmaps and technical decisions without relying on formal reporting lines. Your ability to lead, coach, mentor, and develop team members will be essential in building capability and supporting ongoing growth.

What You'll Bring

Candidates for this AVP Data Science position should possess a minimum of 15 years of progressive experience in data science, machine learning, or applied AI, with a substantial track record of building and productionizing large-scale solutions within complex environments. Deep hands-on expertise in modern data science and ML development is essential, coupled with strong software engineering practices including Python, version control, testing, CI/CD, and MLOps. Demonstrated expertise in leveraging AI-assisted programming tools, such as Codex, Claude, or GPT-based coding agents, to enhance developer productivity and engineering quality is highly valued. You must have a proven ability to lead technically across multiple teams, influencing standards, architecture, and delivery without necessarily being a direct line manager. Strong communication skills are paramount, enabling you to translate complex technical concepts effectively for both senior technical and non-technical stakeholders across the organization. Preferred qualifications include experience standing up or leading a Data Science Center of Excellence or Community of Practice, familiarity with cloud-based ML platforms and modern data ecosystems, and a track record of driving measurable improvements in delivery speed, quality, or reuse through tooling, standards, or SDLC modernization. An advanced degree in a quantitative or technical field, such as a Master’s or PhD, is considered a significant asset.