Data Scientist

Starbucks
Fairview, TN, United States
14 days ago
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Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Experience level
Expert
Compensation
$135,000.0 - $180,000.0
Working hours
Regular working hours
Job source

Tech stack

A/B Testing Amazon Web Services Microsoft Azure Big Data Cloud Computing Information Engineering Data Visualization Apache Hadoop Python (Programming Language) Machine Learning Power BI Tableau (Software)
+6 more
Google Cloud Feature Engineering Apache Spark Data Analytics Data Management Data Pipelines

Job description

Starbucks is seeking a Senior Data Scientist to lead advanced analytics that power decisions across our global coffee and food operations. In this role, you will build and deploy machine learning models to optimize store performance, menu mix, loyalty engagement, and supply chain efficiency. You’ll partner with business, marketing, and operations leaders to translate complex data into clear, actionable insights. Using large datasets from retail, mobile, and loyalty channels, you will design experiments, forecast demand, and measure impact. You’ll mentor junior data scientists and help shape Starbucks’ data science best practices in a collaborative, values-driven environment., * Design, build, and deploy machine learning and statistical models for retail, loyalty, and supply chain use cases.

  • Analyze large, complex datasets to generate clear, actionable insights for business stakeholders.
  • Partner with operations, marketing, and product teams to define problems, scope analytics work, and measure impact.
  • Lead A/B tests and other experiments to evaluate new initiatives and optimize customer and store performance.
  • Develop dashboards and data visualizations that communicate trends and recommendations to non-technical audiences.
  • Mentor and guide junior data scientists, sharing best practices in modeling, coding, and experimentation.
  • Contribute to data science standards, tools, and workflows to improve model reliability and scalability.
  • Collaborate with data engineering teams to ensure high-quality data pipelines and model deployment processes.

Requirements

  • Python
  • SQLMachine learning
  • Statistical modeling
  • A/B testing and experimentation
  • Data visualization (e.g., Tableau, Power BI)
  • Big data tools (e.g., Spark, Hadoop)
  • Time series forecasting
  • Cloud platforms (e.g., AWS, GCP, Azure)
  • Feature engineering

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