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.
2944533046-2 Apply for this job now Job Description Starbucks is seeking a Senior/Lead Data Scientist to shape data-driven decisions across our global coffee business. In this role, you will design and deploy advanced statistical models and machine learning solutions to optimize store operations, beverage innovation, supply chain, and customer experience. You will partner with cross-functional teams in marketing, operations, and digital product to translate complex data into clear insights and actionable strategies. You'll mentor junior data scientists, promote best practices in experimentation and analytics, and help scale our Data & Analytics capabilities in a values-driven, inclusive environment. Responsibilities Design, build, and validate statistical and machine learning models to solve complex business problems. Translate ambiguous business questions into analytical approaches, experiments, and measurable outcomes. Partner with marketing, operations, and digital teams to deliver data-driven recommendations that impact strategy and performance. Develop dashboards and visualizations to communicate insights to technical and non-technical stakeholders. Lead and mentor junior data scientists, promoting best practices in coding, modeling, and documentation. Drive experimentation frameworks, including A/B tests, to evaluate initiatives and optimize customer experiences. Collaborate with data engineering to productionize models and improve data quality and accessibility. Stay current on emerging data science methods and tools and apply them where they add business value. Required Skills Machine learning Statistical modeling Python RSQLData visualization (e.g., Tableau, Power BI) A/B testing and experimentation Big data tools (e.g., Spark, Hadoop) Cloud analytics platforms (e.g., AWS, Azure, GCP) Feature engineering Predictive analytics Time series forecasting Data storytelling Model deployment and MLOps
