Amazon Data Scientist AMZL Strategic Planning 

United States, Washington, Bellevue 

183756390

AMZL Strategic Planning is seeking an experienced Data Science leader, to lead the development of algorithmic tools, supporting Strategic Planning.Key job responsibilities
In this role, you will work with other Research Scientists, Data Scientists, BIEs and DEs and with Business Leaders. You develop your own scientific approaches and partner with teams as they deploy algorithmic solutions to our stakeholders. You will partner with business leaders in evaluating long-term strategic AMZL network choices, speed initiatives, topology build plan optimization. You present strategy papers to our senior-most leaders to drive long-term impact. To accomplish this, we expect you to have a strong research background in at least one of the following disciplines: Operations Research, Operations Management, Statistics, or applied mathematics. You should also have strong business domain knowledge in transportation, distribution operations, logistics management with some knowledge of inventory management theory and practice. You should have a publication record demonstrating technical depth, as well as, consideration of practical aspects in your work. Experience partnering with companies on high-stakes R&D or consulting is a plus.A day in the life
- Design, develop complex mathematical, simulation and optimization models ; apply them to define strategic optimization needs- Apply theories of mathematical optimization, including linear programming, integer programming, dynamic programming, network flows and algorithms to design optimal solutions
- Prototype these models by using modeling languages such as R, Scala, Python. Be conversant with foundational elements of Generative AI, AWS stack

- 4+ years of data scientist experience
- 3+ years of data querying languages (e.g. SQL), scripting languages (e.g. Python) or statistical/mathematical software (e.g. R, SAS, Matlab, etc.) experience
- 3+ years of machine learning/statistical modeling data analysis tools and techniques, and parameters that affect their performance experience
- Experience applying theoretical models in an applied environment