A Data Scientist utilizes skills and experience to systematically answer questions using data to provide actionable recommendations. Commonly utilizes advanced statistical analysis and machine learning techniques. Common responsibilities also include data cleaning and data management.
2944533024-2 Apply for this job now Job Description Starbucks is seeking a Senior/Lead Data Scientist to drive data-informed decisions across our global coffee and food service business. In this role, you will build predictive and optimization models to improve store operations, customer experience, and supply chain efficiency. You will lead analytics projects from problem framing to deployment, partnering with business, tech, and retail stakeholders. Responsibilities include designing experiments, developing machine learning solutions, creating scalable data pipelines, and presenting insights to leadership. You'll mentor junior data scientists and help shape Starbucks' data and analytics strategy in a values-driven, inclusive culture. Responsibilities Lead end-to-end development of predictive and machine learning models to support operations, marketing, and supply chain decisions Partner with cross-functional teams to translate business problems into analytical solutions and measurable outcomes Design and analyze experiments (A/B tests) to evaluate initiatives and optimize performance Build and maintain scalable data pipelines and analytical datasets using SQL and modern data platforms Develop clear data visualizations and presentations for technical and non-technical stakeholders, including senior leadership Mentor and guide junior data scientists and analysts, promoting best practices in modeling and analytics Contribute to the data and analytics roadmap, recommending tools, methods, and standards Ensure data quality, governance, and responsible AI practices in all modeling efforts Required Skills Python SQLMachine learning Statistical modeling Predictive analytics A/B testing and experimentation Data visualization (e.g., Tableau, Power BI) Cloud platforms (e.g., AWS, Azure, GCP) Data engineering / ETLOptimization and forecasting
