data scientist, Global Supply Chain

Key Skills

AWS, Azure, Data Analysis, Data science techniques, ETL frameworks, Machine learning techniques, Python Programming, R Programming, Supply Chain Management

Job Description

As a Data Scientist in the Data, Analytics, Insights, and Business Operations team, you will work cross-functionally with data engineers, data scientists, business experts, and external vendors to help Starbucks make decisions better by using data and analytics. On the Global Supply Chain (GSC) Data Science team, you will need to develop strong relationships across the organization and help advance better, data-driven supply chain decisions that drive enterprise value in the form of enhanced service levels, partner time or cost savings, improved customer and partner experience, risk mitigation and business growth.

As a data scientist, you will… 

• Evaluate unstructured questions from business teams and translate into data problems
• Apply machine learning and 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 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
• Work independently and through ambiguous situations to build relevant analytic solutions using quantitative approaches
• Leverage presentation skills to be able to share insights and recommendations at a leadership level and lead more detailed technical reviews with the analytics community

We’d love to hear from people with:

• MS+ with concentration in quantitative discipline – Operations Research, Stats, Math, Comp Sci, Engineering, Econ, or similar discipline
• Experience with integer programming, local search heuristics, and related Operations Research tools (e.g. Gurobi, CPLEX, XPRESS)
• 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
• Strong competency and expertise in Python, R, or some combination
• 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 / machine learning 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.
• Experience on Cloud platforms such as Azure, AWS, preferred