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.
2944532813-2 Apply for this job now Job Description Starbucks is seeking a Senior/Lead Data Scientist to drive data-informed decisions across our global coffee business. In this role, you will lead advanced analytics, build predictive and optimization models, and turn complex data into clear insights that shape store operations, customer experience, and supply chain. You will partner with cross-functional teams to design experiments, analyze loyalty and sales data, and develop scalable data products. You'll mentor junior data scientists and champion best practices in MLOps, model governance, and responsible AI in a collaborative, values-driven environment. Responsibilities Lead design, development, and deployment of predictive and optimization models for customer, store, and supply chain use cases. Translate ambiguous business questions into clear analytical problems, hypotheses, and measurable success metrics. Design and analyze experiments (A/B tests) to evaluate promotions, product launches, and operational changes. Build robust data pipelines and collaborate with engineering to productionize models and analytics solutions. Develop dashboards and visualizations that clearly communicate insights to technical and non-technical stakeholders. Mentor and guide junior data scientists, promoting best practices in modeling, coding, and documentation. Ensure model governance, monitoring, and responsible AI practices, including fairness and bias assessments. Collaborate with cross-functional partners in marketing, operations, finance, and digital teams to drive data-informed decisions. Required Skills Python RSQLMachine learning Statistical modeling Experiment design / A-B testing Data visualization (e.g., Tableau, Power BI) Cloud analytics platforms (e.g., AWS, GCP, Azure) MLOps and model deployment Big data tools (e.g., Spark, Hadoop)
