Core AI is an interdisciplinary team on the cutting edge of economics, statistical analysis, and machine learning whose mission is to solve AI and ML problems that have high risk with abnormally high returns. Through our efforts we seek to understand and design Amazon's complex network of buyers and sellers, while also leveraging the strengths of our engineers and scientists to build solutions for some of the toughest business problems here at Amazon.
We are looking for a Sr. Principal Economist who is able to provide structure, including sourcing, defining, and leveling, around complex and varied business problems. We seek 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.
This role is to work within the Core AI team in two key ways. The first is working with Amazon's Scholar Residency Program, which consists of world-class academic scientists and researchers collaborating with internal partner teams to facilitate the delivery of Scholar led science into engineering systems. The second is to leverage data, engineering, and science on rapid proofs of concept while filling gaps that may arise for both the Scholars program and other Core AI initiatives.
· Work with Scholars and other Core AI teams to consult with internal partner terms on collaboration opportunities and to assess potential for impact.
· Work with Amazon intern teams to on mechanism design, metrics creation, and technical/scientific strategy.
· With applied science teams - build models, simulations, and ML infrastructure to support them
· Effectively communicate models and tradeoff decisions to business teams and incorporate feedback into project analysis/modeling.
· PhD in Economics or Statistics
· 10+ years combined experience in academic research and industry roles
· Deep expertise in applied econometrics, game theory, market/mechanism design, and empirical IO
· Strong publication and presentation record in top-tier economic, statistical, or ML publications and conferences
· Coding ability in a scripting language such as R, Python, or STATA
· Background in engineering and working with large scale machine learning
· Depth of knowledge in causal inference / experimental design
· Thinks strategically, but stays on top of tactical execution
· Effective verbal and written communications skills
· Has relentlessly high standards
· Dives deep, thinks big and has convictions