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# Lead Decision Scientist

- **Company:** Starbucks
- **Location:** Springfield, TN, United States
- **Experience:** Expert
- **Salary:** $135,000.0 - $180,000.0
- **Contract:** Permanent contract
- **Skills:** A/B Testing, Amazon Web Services, Business Analytics Applications, Microsoft Azure, Information Engineering, Data Visualization, Machine Learning, Power BI, Cloud Services, Tableau (Software), Google Cloud, Data Analytics
- **Published:** September 10, 2026
- **Apply:** https://find.jobs/jobs-near-me/apply/ats-redirect/?id=2963035184-2

## About the Role

* Statistical modeling
 * Machine learning
 * A/B testing and experimentation
 * Causal inference
 * SQLPython or RData visualization (Tableau/Power BI)
 * Predictive analytics
 * Optimization and forecasting
 * Cloud data platforms (e.g., AWS, Azure, GCP)

## Description

Starbucks is seeking a Senior/Lead Decision Scientist to shape data-driven strategy across our global coffee and food business. In this role, you will design experiments, build predictive and optimization models, and translate complex data into clear insights that guide customer, store, and supply chain decisions. You will partner with leaders across Data & Analytics, operations, and marketing to define problems, frame hypotheses, and measure impact. You'll mentor junior scientists, champion best practices in experimentation and causal inference, and help build scalable analytics solutions that support Starbucks' values of inclusion, sustainability, and community impact., * Lead design and analysis of experiments to optimize pricing, promotions, and customer experiences.

 * Develop and deploy predictive and optimization models to support store, digital, and supply chain decisions.
 * Translate complex analytical findings into clear, actionable recommendations for business leaders.
 * Partner with cross-functional teams in marketing, operations, and product to define problems and metrics.
 * Mentor and guide junior decision scientists and analysts on best practices and methods.
 * Ensure analytical rigor, data quality, and reproducibility across projects.
 * Leverage visualization tools to communicate insights to technical and non-technical stakeholders.
 * Collaborate with data engineering teams to operationalize models and analytics solutions.
 * Monitor model performance and iterate based on impact and changing business needs.
 * Support a culture of experimentation, ethical data use, and continuous improvement aligned with Starbucks values.