Starbucks is looking to optimize its global supply chain network to drive resilience, cost efficiency, and service by developing and deploying analytical solutions.
Requirements
- Strong background working with predictive and statistical modeling, machine learning, optimization and strong expertise in all phases of the modeling pipeline
- Experience building complex data sets from multiple data sources, both internally and externally
- Strong SQL, database and ETL skills required including cleaning and managing data
- Advanced competency and expertise in Python, R or some combination
- Experience with integer programming, local search heuristics, and related OR tools (e.g. Gurobi, CPLEX, XPRESS)
- Experience on Cloud platforms such as Azure, AWS, preferred
- Experience with multi-echelon network design and inventory optimization, preferred
Responsibilities
- Formulate, develop, and deploy optimization, simulation, and mathematical models addressing: Distribution network topology, design and long-range planning to achieve cost and service objectives
- Multiechelon inventory flow
- Transportation routing, scheduling and mode optimization
- Final mile replenishment to our coffeehouses
- Evaluate unstructured questions from business teams and translate into data problems
- Apply statistical knowledge to create optimization models & predictive analyses
- Utilize systems thinking approach to conceive, plan, and build new data products
Other
- MS+ with concentration in quantitative discipline – Operations Research, Stats, Math, Comp Sci, Engineering, Econ, or similar discipline
- Ability to apply knowledge of multidisciplinary business principles and practices to achieve successful outcomes in cross-functional projects and activities
- Ability to educate others on statistical / optimization modeling methods
- Self-starter, attention to details and results orientated, able to work under minimal guidance
- Proficient in communicating effectively with both technical and nontechnical stakeholders
