Manager, Data Science- Merchandising Category Analytics

Manager, Data Science – Merchandising Category Analytics

Global Data & Analytics | Alimentation Couche-Tard

Location: Charlotte, NC

JOIN OUR TEAM! At Circle K, our mission is simple: to make our customers’ lives a little easier every day. You may have already stopped for coffee, refueling your car, or eating something on the go. Then, you know what Circle K is. Since the opening of a first store in Laval, Québec, our parent company, Alimentation Couche-Tard has never ceased to grow. We have grown into a successful global company with over 17,300 stores in 29 countries, serving almost 8.5 million customers every day. In total, more than 146,000 people work in our stores and offices.  We make journeys easier by offering fast and friendly service. We care about our people and our communities, and we look for ways to uplift people first. Wherever your journey’s going, we can help you get there.   Are you ready to grow your career? Let's grow together!

We are seeking an innovative, dynamic Data Science Manager to lead Data Science, Machine Learning, and AI initiatives for Merchandising Category Analytics. As a key leader within the Global Data & Analytics team, you will partner with Merchandising, Category Intelligence & Enablement (CIE), and Technology teams to translate strategic priorities into scalable, data-driven solutions that improve decisions and business performance.

The ideal candidate brings deep technical expertise, commercial acumen, product thinking, and strong people leadership. You will lead a high-performing team of data scientists, analysts, and machine learning engineers, taking solutions from opportunity identification through deployment, adoption, and measurable value realization.

• →Shape the Data Science and AI portfolio: Develop and execute initiatives aligned with merchandising and enterprise priorities.
• →Partner with business leaders: Work with Merchandising and CIE leaders to identify high-value opportunities, define business problems, and translate needs into analytics and AI solutions.
• →Influence decisions: Present actionable insights and recommendations to senior leaders to shape business strategy and strengthen value realization.

• →Lead solution development: Guide the design and deployment of scalable solutions across pricing, assortment, promotions, forecasting, experimentation, optimization, customer insights, and category performance.
• →Advance AI and ML innovation: Evaluate and apply emerging capabilities, including generative AI and AI agents, to improve merchandising decisions and productivity.
• →Ensure end-to-end delivery: Lead initiatives from data discovery and experimentation through production deployment, adoption, governance, and value realization.
• →Scale capabilities: Establish reusable methodologies, frameworks, and MLOps practices that enable solutions to scale across categories and business areas.

• →Measure business value: Define success metrics and demonstrate the measurable impact of Data Science, ML, and AI investments.
• →Lead and develop talent: Coach and develop data scientists and analysts while fostering technical excellence, innovation, collaboration, and continuous learning.
• →Collaborate across Data & Analytics and Technology: Partner with CIE, Data Engineering, Data Products, and Technology teams to deliver scalable, governed solutions aligned with enterprise standards.
• →Champion responsible AI: Promote strong practices for model governance, explainability, data quality, privacy, and responsible AI adoption.

• Master’s degree in Data Science, Computer Science, Statistics, Mathematics, Engineering, or a related quantitative discipline; an advanced business degree is an asset.
• At least eight years of experience in Data Science, Machine Learning, Advanced Analytics, or a related field, including three or more years in leadership roles delivering large-scale initiatives.

• Strong knowledge of exploratory and descriptive analytics, hypothesis testing, A/B testing, experiment design, and Agile development practices.
• Proficiency in Python, SQL, and R, with hands-on experience using cloud data platforms such as Azure, Snowflake, or Databricks.
• Working knowledge of data engineering workflows, including ETL, batch and real-time processing, model deployment, MLOps, and version control.

• Strong commercial mindset and demonstrated ability to translate data, analytics, and AI/ML insights into clear decisions, strategies, and business outcomes.
• Proven experience in talent acquisition, team development, coaching, and mentoring.
• Excellent communication and stakeholder management skills, with the ability to explain technical concepts to audiences at all levels and influence senior leaders across geographies.
• Demonstrated success collaborating with cross-functional partners, including Data Engineering, Architecture, and Data Cloud Platform teams.
• Ability to prioritize multiple initiatives, communicate timelines clearly, and drive work proactively to completion.
• Adaptability, curiosity, and a commitment to continuous learning across business and technology domains.

Success will be measured by the ability to connect merchandising strategy with Data Science, ML, and AI capabilities; deliver scalable solutions with measurable business impact; and build a high-performing data science organization.

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