Why This Role Stands Out
This hybrid Senior/Lead Data Scientist role at Starbucks offers incredible growth potential as you'll spearhead impactful analytics and machine learning initiatives to shape the future of a beloved global brand. You'll thrive here if you're passionate about leveraging data to optimize operations and customer experiences, mentoring others, and driving strategic data decisions within a values-driven company. Apply now to join a dynamic team and contribute to Starbucks' continued success!
Quick Overview
Job Description
Starbucks is seeking a Senior/Lead Data Scientist to drive data-informed decisions across our global coffee business. In this role, you'll lead advanced analytics, experimentation, and machine learning projects that optimize store operations, pricing, and customer experience. You'll partner with cross-functional teams to turn complex data into clear insights, build predictive models, and guide data strategy. You will mentor other data scientists, champion data quality and governance, and help scale analytics capabilities that support sustainable growth, ethical sourcing, and our values-driven mission.
Responsibilities
- Lead end-to-end analytics and machine learning projects from problem framing to deployment and measurement
- Develop predictive and prescriptive models to optimize customer experience, operations, and pricing
- Design and analyze experiments and A/B tests to evaluate initiatives and inform strategy
- Collaborate with product, operations, marketing, and finance teams to translate business needs into data solutions
- Build dashboards and visualizations that communicate complex insights clearly to non-technical stakeholders
- Ensure data quality, governance, and reproducibility across analytics workflows
- Mentor and guide junior data scientists and analysts, sharing best practices and standards
- Contribute to the long-term data and analytics strategy, tooling, and model lifecycle management
Required Skills
- Python
- RSQLMachine learning
- Statistical modeling
- A/B testing and experimentation
- Data visualization (Tableau/Power BI)
- Cloud platforms (AWS/Azure/GCP)
- Data wrangling and ETLBig data tools (Spark/Hadoop)
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