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.
2944532876-2 Apply for this job now Job Description Starbucks is hiring a Senior/Lead Data Scientist to drive data-informed decisions across our global coffee business. In this role, you will lead advanced analytics and machine learning projects to optimize store operations, personalization, supply chain, and customer experience. Partner with cross-functional teams to translate business questions into data solutions, build production-ready models, and develop dashboards and experimentation frameworks. You will mentor junior data scientists, champion data quality and governance, and help shape Starbucks' data & analytics strategy with a focus on ethical, responsible AI and measurable business impact. Responsibilities Lead end-to-end design, development, and deployment of machine learning and advanced analytics solutions. Partner with business stakeholders to translate problems into data science projects with clear success metrics. Analyze large, complex datasets to generate insights that improve operations, personalization, and supply chain efficiency. Develop dashboards, reports, and data products that enable self-service analytics for business partners. Design and evaluate experiments (e.g., A/B tests) to measure impact of new initiatives. Mentor and guide junior data scientists and analysts, promoting best practices in modeling and coding. Ensure data quality, reproducibility, and governance across analytics workflows. Collaborate with data engineering teams to improve data pipelines, features, and model performance. Communicate complex analytical findings clearly to technical and non-technical audiences. Contribute to Starbucks' data & analytics strategy with a focus on ethical and responsible AI. Required Skills Python SQLMachine learning Statistical modeling Experiment design (A/B testing) Data visualization (Tableau/Power BI/Looker) Cloud data platforms (AWS/Azure/GCP) Feature engineering Big data tools (Spark/Hadoop) Model deployment/ML Ops
