senior decision scientist, Quantitative Science at Starbucks Coffee Company
Seattle, Washington, United States -
Full Time


Start Date

Immediate

Expiry Date

15 Apr, 26

Salary

0.0

Posted On

15 Jan, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

No

Skills

Causal Inference, Experimentation Frameworks, Forecasting, Inventory Optimization, Supply Chain Planning, Data Set Building, Root Cause Analysis, Statistical Methods, Machine Learning, Cloud Platforms, Azure, AWS, Databricks, AI-assisted Capabilities, Quick Service Restaurant Management, Retail Inventory Management

Industry

Retail

Description
Develop causal inference and experimentation frameworks to quantify the impact of operational or assortment changes All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, age, national origin, disability, or protected veteran status, or any other characteristic protected by law. Qualified applicants with criminal histories will be considered for employment in a manner consistent with all federal, state and local ordinances. Experience with forecasting, inventory optimization, and supply chain planning Experience building complex data sets from multiple data sources, both internally and externally Experience root causing complex and ambiguous current state outcomes and trends and tying to specific technical and nontechnical resolutions 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 Proficient in communicating effectively with both technical and nontechnical stakeholders. Experience with Cloud platforms such as Azure, AWS, Databricks, and AI-assisted capabilities preferred Experience with multi-echelon inventory optimization preferred Experience specific to quick service restaurant or retail inventory management beneficial, not required
Responsibilities
Develop causal inference and experimentation frameworks to quantify the impact of operational or assortment changes. The role involves analyzing complex data sets and providing insights for decision-making.
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