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Restaurant Brands International Inc. is one of the world's largest quick service restaurant companies with nearly $45 billion in annual system-wide sales and over 32,000 restaurants in more than 120 countries and territories.

RBI owns four of the world's most prominent and iconic quick service restaurant brands – TIM HORTONS®, BURGER KING®, POPEYES®, and FIREHOUSE SUBS®. These independently operated brands have been serving their respective guests, franchisees and communities for decades. Through its Restaurant Brands for Good framework, RBI is improving sustainable outcomes related to its food, the planet, and people and communities.

RBI is committed to growing the TIM HORTONS®, BURGER KING®, POPEYES® and FIREHOUSE SUBS® brands by leveraging their respective core values, employee and franchisee relationships, and long track records of community support. Each brand benefits from the global scale and shared best practices that come from ownership by Restaurant Brands International Inc.

Tim Hortons® is one of North America's largest restaurant chains operating in the quick service segment, with more than 4,800 system wide restaurants located in Canada, the United States and around the world.

We are currently looking for experienced and driven data scientist to join our Advanced Analytics team. The Advanced Analytics team is responsible for the building, training, deploying, scoring, and monitoring of machine learning models for various use cases and across different functional areas, including but not limited to personalized recommender, forecasting, and LLM modelling. The data scientist will play a pivotal role in advancing our advanced analytics and machine learning capabilities within the organization.

We are looking for a Data Scientist who thrives at the intersection of data, machine learning, and business impact. You will play a critical role in translating complex business needs into analytical and machine learning solutions, while partnering closely with Machine Learning Engineers to ensure models are reliable, scalable, and delivering value in production.

RBI follows a 5 day, in-office work schedule to support collaboration. Candidates should be comfortable working onsite 5 days per week out of our office inToronto, ON.

What You’ll Do:

• Drive continuous improvement of business performance through advanced analytical techniques, including but not limited to multivariate analysis, key driver analysis, and exploratory data analysis.
• Translate analytical insights into actionable recommendations for business and product teams.
• Monitor machine learning model performance daily and identify performance issues.
• Conduct A/B testing and analyze results to support model improvements.
• Partner with ML engineers to maintain and refine models in production.

Who You Are:

• Own and drive data science initiatives in a fast-paced, evolving QSR environment, from problem framing to measurable business outcomes.
• Translate complex and ambiguous business requirements into clear analytical approaches, models, and insights on a daily basis.
• Collaborate closely with Machine Learning Engineers and Product teams to support model deployment, monitoring, performance tracking, and ongoing maintenance in production.
• Focus on outcomes by analyzing, visualizing, and interpreting data to inform decisions and drive continuous improvement across the business.
• Operate effectively in an Agile environment, maintaining transparency, accountability, and clear communication with both technical and non-technical stakeholders.
• Continuously learn and adapt in ambiguous problem spaces, contributing with a collaborative and solution-oriented mindset.

What You Bring:

• Bachelor’s degree or equivalent experience in a quantitative or technical field.
• 3+ years of experience working as a Data Scientist or in a similar analytical role with production of ML models.
• Strong Python skills and experience with data platforms such as PySpark and/or Databricks.
• Hands-on experience building and supporting machine learning models using common ML frameworks.
• Solid understanding of core data science methods, including regression, classification, clustering, and time series analysis.
• Strong communication skills with the ability to explain technical concepts to non-technical stakeholders.
• Strong SQL skills for analyzing and transforming large datasets.
• Experience monitoring model performance and supporting model lifecycle management; MLOps or LLMOps experience is a plus.
• This position will be responsible for joining the model on-call rotation to monitor, troubleshoot, and maintain production ML models.
• Familiarity with cloud platforms, preferably AWS, is an asset.

Base salary range of $100,000 – $140,000 CAD. Base salary for this position may vary based on relevant factors such as experience, education, location, and skills.

This position is eligible to participate in the Company’s annual discretionary bonus plan, subject to the terms and conditions of the incentive program, based on individual and company performance metrics. This position may also be eligible for additional compensation in the form of equity grants pursuant to the Company’s long-term incentive plan.

This posting is for an upcoming vacancy.

#TimHortons

Benefits at all of our global offices are focused on physical, mental and financial wellness. We offer unique and progressive benefits, including a comprehensive global paid parental leave program that supports employees as they expand their families, free telemedicine and mental wellness support.

Restaurant Brands International and all of its affiliated companies (collectively, RBI) are equal opportunity and affirmative action employers that do not discriminate on the basis of race, national origin, religion, age, color, sex, sexual orientation, gender identity, disability, or veteran status, or any other characteristic protected by local, state, provincial or federal laws, rules, or regulations. RBI's policy applies to all terms and conditions of employment. Accommodation is available for applicants with disabilities upon request.