Amazon strives to ensure that customers have access to a wide selection of goods, and that those goods meet all safety and regulatory standards. A new economics team launched by PARS-Tech (Product Assurance, Risk, and Security) will proactively study process improvements that maintain selection while simultaneously ensuring safety of our customers, partners, and Associates.
The team is small but influential, and will work with the broader PARS team to develop science in this new area. A unique highlight of this team is that it offers an all-encompassing glimpse of the logistical chain, including: product development, transport across borders, storage and handling in fulfillment centers, information presentation on the detail page, shipping and returns. The team will be incubated for the first year inside of Core AI, an interdisciplinary team working 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. Core AI offers access to world-class academic scientists and researchers that will consult and collaborate in developing solutions.
The ideal candidate will be able to: (1) use economic theories to structure ambiguous problems; (2) define proper metrics and assemble unique datasets to test hypotheses; (3) assess and interpret (causal) empirical relationships using data; and (4) design and test how incentives affect behavior, taking into account dynamic long-term and strategic factors. 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.
· PhD in Economics or highly related field.
· 2-5 years of experience in industry, government, or academic research.
· Experience in applied economic analysis.
· Experience in using one of Stata, R, or Python is required.
· Strong research track record, drives results.
· Effective verbal and written communications skills.
· Exhibits business judgment.
· Has relentlessly high standards.