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.
2944530399-2 Apply for this job now Job Description Starbucks is seeking a Senior Data Scientist to lead advanced analytics that power decisions across our global coffee and food operations. In this role, you will build and deploy machine learning models to optimize store performance, menu mix, loyalty engagement, and supply chain efficiency. You'll partner with business, marketing, and operations leaders to translate complex data into clear, actionable insights. Using large datasets from retail, mobile, and loyalty channels, you will design experiments, forecast demand, and measure impact. You'll mentor junior data scientists and help shape Starbucks' data science best practices in a collaborative, values-driven environment. Responsibilities Design, build, and deploy machine learning and statistical models for retail, loyalty, and supply chain use cases. Analyze large, complex datasets to generate clear, actionable insights for business stakeholders. Partner with operations, marketing, and product teams to define problems, scope analytics work, and measure impact. Lead A/B tests and other experiments to evaluate new initiatives and optimize customer and store performance. Develop dashboards and data visualizations that communicate trends and recommendations to non-technical audiences. Mentor and guide junior data scientists, sharing best practices in modeling, coding, and experimentation. Contribute to data science standards, tools, and workflows to improve model reliability and scalability. Collaborate with data engineering teams to ensure high-quality data pipelines and model deployment processes. Required Skills Python SQLMachine learning Statistical modeling A/B testing and experimentation Data visualization (e.g., Tableau, Power BI) Big data tools (e.g., Spark, Hadoop) Time series forecasting Cloud platforms (e.g., AWS, GCP, Azure) Feature engineering
