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Data Scientist

Job in Adairville, Logan County, Kentucky, 42202, USA
Listing for: Starbucks
Full Time position
Listed on 2026-08-29
Job specializations:
  • IT/Tech
    Data Analyst, Data Scientist
Salary/Wage Range or Industry Benchmark: 120000 - 165000 USD Yearly USD 120000.00 165000.00 YEAR
Job Description & How to Apply Below
Location: Adairville

Starbucks is seeking a Data Scientist to join our Data & Analytics team supporting our global coffee retail and store operations. In this role, you will analyze large, complex datasets from stores, loyalty programs, supply chain, and digital channels to generate insights that improve customer experience, store performance, and operational efficiency. You will build predictive models, conduct A/B testing, and create dashboards that guide decision-making.

You'll partner with business, marketing, and operations teams and help embed data-driven thinking across Starbucks in an inclusive, values-driven culture.

Responsibilities

  • Analyze large, complex datasets from stores, loyalty, and digital channels to uncover actionable insights.
  • Develop, validate, and deploy predictive and machine learning models to support key business decisions.
  • Design and analyze A/B tests and experiments to measure the impact of new initiatives.
  • Build clear dashboards and reports to communicate performance trends to stakeholders.
  • Collaborate with marketing, operations, and finance teams to translate business questions into analytical solutions.
  • Clean, transform, and validate data to ensure accuracy, consistency, and reliability.
  • Present findings and recommendations to technical and non-technical audiences.
  • Partner with engineering teams to product ionize models and analytics solutions.
  • Monitor model performance and iterate based on business feedback and new data.
  • Support a culture of data literacy and data-driven decision-making across Starbucks.

Required Skills

  • Python
  • RSQLMachine learning
  • Statistical modeling
  • Data visualization (Tableau/Power BI)
  • A/B testing and experimentation
  • ETL and data wrangling
  • Cloud data platforms (AWS/Azure/GCP)
  • Dashboard development
  • Time-series analysis
  • Predictive modeling
  • Big data tools (Spark/Hadoop)
  • Version control (Git)
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