Why This Role Stands Out
This hybrid role at Starbucks offers a unique opportunity to leverage your advanced analytics and machine learning expertise to make a significant impact on a globally recognized brand, driving innovation in customer experience and operational efficiency. You'll thrive here if you enjoy leading impactful projects, mentoring others, and contributing to a company's data strategy in a collaborative and values-driven culture. Apply to grow your career with a leader in the coffee industry!
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 will lead advanced analytics and machine learning projects that optimize store operations, personalize customer experiences, and support ethical sourcing and sustainability initiatives. You will partner with cross-functional teams in marketing, supply chain, and operations to translate complex data into clear insights and scalable solutions. You will mentor junior data scientists, champion data quality and experimentation, and help shape Starbucks' long-term data and analytics strategy in a collaborative, values-driven environment.
Responsibilities
- Lead design and delivery of advanced analytics and machine learning solutions to support customer, store, and supply chain decisions.
- Partner with marketing, operations, and supply chain to frame business problems and translate them into analytical projects.
- Develop, validate, and deploy predictive and prescriptive models, ensuring scalability and reliability in production.
- Design and analyze A/B tests and experiments to measure the impact of new initiatives and product changes.
- Build dashboards and visualizations that communicate complex insights to non-technical stakeholders.
- Mentor and coach junior data scientists and analysts, promoting best practices in modeling, coding, and documentation.
- Ensure data quality, governance, and responsible use of data, aligned with Starbucks' values and customer trust.
- Contribute to the data & analytics roadmap, evaluating new tools, methods, and technologies to improve capabilities.
Required Skills
- Python
- RSQLMachine learning
- Statistical modeling
- Data visualization (e.g., Tableau, Power BI)
- Big data tools (e.g., Spark, Hadoop)
- A/B testing and experimentation
- Cloud platforms (e.g., AWS, GCP, Azure)
- Feature engineering
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