Starbucks is looking to solve complex supply chain challenges by using data and analytics to drive enterprise value in the form of enhanced service levels, time or cost savings, improved customer and partner experience, risk mitigation and business growth.
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
- 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
- Drive adoption of data products and insights across business teams by visualizing results and crafting effective communications to share discoveries
- Lead data science solutions as an embedded technical expert from beginning to end
- Build relationships with a wide-range of business partners and establish yourself and the team as subject matter experts in supply chain analytics
- Ability to work independently and through ambiguous situations to build relevant analytic solutions using quantitative approaches
Other
- MS+ with concentration in quantitative discipline – Operations Research, Stats, Math, Comp Sci, Engineering, Econ, or similar discipline
- 3+ years industry experience in data science
- 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
