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Starbucks

Data Scientist

Pay: $132,000–$185,000/yr

Posted: 6 days ago

Location: 2 locations (Onsite)

Last Updated: 1 day ago

Hours: Full-Time

Expires: 10/3/2026

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Job Description

Data Scientist Date Posted:

29 August 2026

Closing Date:

September 28, 2026

Recruiter:

Starbucks Location:

Chapel Hill, Tennessee Salary:

USD132,000

USD185,000

Job Type & Industry:

Data Science Contract Type:

Permanent Job Reference:

2944532567-2 Apply for this job now Job Description Starbucks is seeking a Data Scientist to join our Data & Analytics team supporting global retail and beverage operations. You will use statistical modeling, machine learning, and experimentation to optimize store performance, personalize customer experiences, and improve supply chain efficiency. Partner with cross-functional stakeholders to translate business questions into analytical projects, build and validate predictive models, and deliver actionable insights through dashboards and clear storytelling. You'll help shape data strategy, champion data quality and governance, and contribute to a culture of ethical, responsible data use across Starbucks. Responsibilities Collaborate with business stakeholders to define analytical problems and success metrics. Design, build, and validate statistical and machine learning models for retail, customer, and supply chain use cases. Query and transform large datasets using SQL and scripting languages. Develop dashboards and reports to visualize performance and model outcomes. Run A/B tests and experiments to measure the impact of initiatives. Communicate complex findings through clear presentations and data storytelling. Support data governance, quality checks, and documentation of analytical workflows. Partner with engineering teams to operationalize models and analytics solutions. Monitor model performance and iterate based on new data and business feedback. Contribute to a culture of ethical and responsible data use aligned with Starbucks values. Required Skills Python RSQLMachine learning Statistical modeling Data visualization A/B testing and experimentation Predictive analytics Data wrangling Dashboard tools (Tableau/Power BI)