Director, Applied Science, Stores Economics and Science
Company : Amazon
Location : Mountain View, CA, 94043
Posted Date : 28 October 2025
Job Type : Other
Category : Management
Occupation : Director
Job Details
Science Leader Opportunity
We are looking for a science leader who can provide structure around complex business problems, work with machine learning scientists to estimate and validate their models on large scale data, and help business and tech partners turn the results of their analysis into policies, programs, and actions that have a major impact on Amazon's business. We are looking for creative thinkers who can combine a strong economic toolbox with a desire to learn from others, and who know how to execute and deliver on big ideas.
At the senior level, you will own the development of scientific models and manage, in close collaboration with scientists and engineers, the data analysis, modeling, and experimentation necessary for estimating and validating your model. You will need to work with our business partners to communicate the properties of your analysis/modeling and be able to work to incorporate their feedback and requests into your project. Experience in applied economic analysis is essential, and you should be familiar with modern tools for data science and business analysis.
The Stores Economics and Science (SEAS) organization uses economics, statistics, and machine learning to understand and design the complex economy of Amazon's network of buyers and sellers. We are an interdisciplinary team, committed to using cutting edge technology and leveraging the strengths of engineers and scientists to build solutions for some of the toughest business problems at Amazon. Our team is composed in equal parts of software engineers and scientists trained in diverse fields including ML, robotics, computer vision (CV), natural language processing (NLP), distributed systems, and econometrics.
Our mission is to drive adoption of AI science and technology across Amazon by partnering with business teams on science-heavy problems, incubating products with high AI research risk, reducing friction and manual effort with AI tooling, and raising the scientific bar across all teams at Amazon.
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