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# Lead Data Scientist

- **Company:** Starbucks
- **Location:** Nolensville, TN, United States
- **Experience:** Expert
- **Salary:** $155,000.0 - $210,000.0
- **Contract:** Permanent contract
- **Skills:** A/B Testing, Amazon Web Services, Business Analytics Applications, Microsoft Azure, Data Visualization, Apache Hadoop, Python (Programming Language), Power BI, Tableau (Software), Google Cloud, Apache Spark, Data Analytics, Data Management, Machine Learning Operations, Data Pipelines
- **Published:** September 9, 2026
- **Apply:** https://find.jobs/jobs-near-me/apply/ats-redirect/?id=2963036581-2

## About the Role

* 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)

## 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., * 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.


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