Location:
Toronto
Address:
100 Queens Quay East Toronto, Ontario M5E 1V3 Canada
Job Title:
Data Scientist
Canada Goose isn’t like anything else. We’ve built something great, something special - an iconic lifestyle brand with an inspirational and authentic story. At the heart of it is our promise to inspire and enable all people to thrive in the world outside. To Live in the Open. At Canada Goose, you’re part of a movement that belongs to something bigger. One that seeks out the restorative power of nature and is driven by a purpose to keep the planet cold and the people on it warm. We endure any condition, observe every detail, and are building a community that believes in living bravely and coming together to support game-changing people.
Here, opportunities are everywhere - to try something new, to learn, to do meaningful and impactful work, and they’re yours for the taking.
Position Overview:
The Data Scientist is a critical contributor to unlocking insights and driving data-informed decisions across Canada Goose. In this role, you will design, develop, and deploy advanced analytics and machine learning solutions, ensuring the accuracy, integrity, and value of corporate data. Through collaboration with data analysts, data engineers, vendors, and key business stakeholders, you will translate complex data into actionable recommendations that enable digital transformation, operational efficiency, and strategic growth.

• Data Analysis & Insights: Analyze large, complex datasets to uncover trends, patterns, and actionable insights that drive business decision-making.
• Model Development: Design, build, and validate predictive models and machine learning algorithms to solve real-world business challenges.
• KPIs & Metrics: Design and develop business metrics to measure and drive performance across various domains, including customer value (e.g., CLTV – customer lifetime value), marketing effectiveness (e.g., Marketing Mix Modeling, Multi-Touch Attribution), financial performance (e.g., profitability metrics, IRR – Internal Rate of Return, NPV – Net Present Value).
• Data Pipeline Engineering: Develop and maintain robust data pipelines for the collection, processing, and transformation of structured and unstructured data.
• Collaboration: Partner closely with stakeholders across business, technology, and analytics teams to understand requirements and develop data-driven solutions.
• Data Visualization: Create clear, compelling visualizations and dashboards to communicate findings and performance metrics to both technical and non-technical audiences.
• Data Governance & Quality: Ensure data integrity, accuracy, and compliance with organizational standards and regulatory requirements.
• Continuous Improvement: Monitor and refine deployed models to ensure accuracy and relevance, proactively identifying opportunities for increased efficiency and performance.
• Documentation: Maintain comprehensive documentation of data sources, analytical methods, and model assumptions to support transparency and reproducibility.
• Innovation: Stay current with emerging data science techniques, tools, and best practices, championing the adoption of new technologies and approaches within the team.