Why This Role Stands Out
This hybrid Senior/Lead Data Scientist role at Starbucks offers a fantastic opportunity to shape data-driven strategies for a globally recognized brand, fostering significant career growth through mentorship and advanced analytics. You will thrive here if you are passionate about leveraging machine learning and experimentation to impact customer experience and optimize operations, all within a collaborative environment. Embrace the chance to influence key decisions and advance your skills at Starbucks.
Quick Overview
Job Description
Starbucks is seeking a Senior/Lead Data Scientist to drive data-informed decisions across our global coffee and food business. In this role, you will build advanced predictive models and experiments to optimize store operations, personalize customer experiences, and support strategic initiatives. You will partner with marketing, supply chain, and store operations to translate data into clear recommendations, design and evaluate A/B tests, and develop dashboards and analytical tools. You will mentor junior data scientists, promote best practices in data science and MLOps, and help shape Starbucks' data and analytics roadmap.
Responsibilities
- Develop and deploy machine learning models to support marketing, store operations, and supply chain decisions
- Design and analyze A/B tests and experiments to evaluate initiatives and product features
- Extract, clean, and transform large datasets using SQL and scripting languages
- Create dashboards and visualizations to communicate insights to non-technical stakeholders
- Partner with cross-functional teams to translate business questions into analytical solutions
- Mentor and guide junior data scientists and analysts on methods and best practices
- Collaborate with data engineering to productionize models and improve data pipelines
- Define and promote standards for model evaluation, monitoring, and documentation
- Contribute to the data and analytics strategy and roadmap for Starbucks
- Present findings and recommendations to senior leadership in clear, actionable terms
Required Skills
- Python
- SQLMachine learning
- Statistical modeling
- A/B testing and experimentation
- Data visualization (e.g., Tableau, Power BI)
- Cloud platforms (e.g., AWS, GCP, Azure)
- Feature engineering
- Predictive analytics
- ML model deployment/MLOps
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