Data Scientist

Starbucks
Gladeville, TN, United States
24 days ago
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Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Compensation
$132,000.0 - $185,000.0
Working hours
Regular working hours
Job source

Tech stack

A/B Testing Business Analytics Applications Big Data Data Governance Data Visualization Python (Programming Language) Machine Learning Power BI SQL Databases Tableau (Software) Data Processing Scripting
+1 more
Data Strategy

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., * 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.

Requirements

  • Python
  • RSQLMachine learning
  • Statistical modeling
  • Data visualization
  • A/B testing and experimentation
  • Predictive analytics
  • Data wrangling
  • Dashboard tools (Tableau/Power BI)

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