Starbucks is looking to develop and enhance forecasting models and optimization algorithms to drive staffing decisions across US and Canada company-owned stores, aiming to improve staffing efficiency and ensure optimal coverage to meet customer demand.
Requirements
- Proficiency in coding languages for data preparation and modeling, including SQL for querying large datasets and Python for building pipelines and statistical models
- Solid understanding of machine learning and statistical techniques, such as regression, classification, decision trees, clustering, and causal inference, with practical experience applying them to real-world problems
- Experience with data visualization tools and techniques (e.g., Tableau, Power BI, Shiny, or D3) to communicate insights effectively to technical and non-technical audiences
- Demonstrated curiosity and analytical rigor, with a strong ability to explore complex datasets, identify patterns, and generate actionable insights
- Experience building forecasting models for time series or operational planning
- Background in optimization techniques such as linear programming, mixed-integer programming, or heuristic algorithms for decision support
- Familiarity with PySpark and Databricks for distributed data processing and scalable analytics in cloud environments
Responsibilities
- Forecast staffing needs across US and Canada stores – You will build and refine predictive models using historical data, store attributes, and external signals to anticipate staffing demand across diverse store formats and regions.
- Conduct exploratory data analysis to inform staffing strategy – You will analyze large-scale operational and workforce datasets to uncover trends, identify inefficiencies, and generate actionable insights that shape staffing system evolution.
- Support the development of optimization algorithms – You will support in design and implement algorithms that recommend optimal staffing levels, balancing labor efficiency with customer experience, and integrate these solutions into production systems.
- Collaborate cross-functionally to evolve the staffing platform – You will partner with engineering, operations, and product teams to translate business needs into scalable data science solutions and continuously improve system performance.
- Influence strategic workforce planning – You will contribute to long-term staffing strategy by providing data-driven recommendations to senior leaders and helping shape the future of staffing across the enterprise.
- Demonstrate curiosity and a growth mindset – Approach complex problems with intellectual curiosity, continuously seek out new features and methodologies, and stay current with industry trends and best practices.
- Thrive in a collaborative, impact-driven team – You will be part of a high-performing, supportive team that values curiosity, innovation, and continuous learning, while driving meaningful business outcomes.
Other
- Bachelors+ with concentration in quantitative discipline – Operations , Stats, Math, Comp Sci, Engineering, or similar discipline. Masters preferred. Minimum of 1 year of experience
- Excellent attention to detail, along with strong written and verbal communication skills to collaborate across cross-functional teams and present findings to stakeholders
- We believe we do our best work when we're together, which is why we're onsite four days a week.
