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# Lead Data Scientist

- **Company:** Starbucks
- **Location:** Chapmansboro, TN, United States
- **Experience:** Expert
- **Salary:** $140,000.0 - $185,000.0
- **Contract:** Permanent contract
- **Skills:** A/B Testing, Amazon Web Services, Microsoft Azure, Information Engineering, Data Presentation, Apache Hadoop, Python (Programming Language), Machine Learning, Power BI, Tableau (Software), Google Cloud, Feature Engineering, Apache Spark, Data Analytics, Data Management, Machine Learning Operations
- **Published:** September 9, 2026
- **Apply:** https://find.jobs/jobs-near-me/apply/ats-redirect/?id=2963036362-2

## About the Role

* 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

## 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., * 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.


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