Manager, Data Science – Merchandising Category Analytics

Global Data & Analytics | Alimentation Couche-Tard

Location: Charlotte, NC

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.