The Research and Measurement team in Amazon Operations HR is seeking an Economist to innovate at the intersection of employee experience data, Operational metrics, HR data and social media data. With a scientific HR organization and the growth trajectory of WW Operations, you will help answer complex business problems that can shape our future.
In collaboration with data, research scientists and engineers, you will build applied micro econometric models using our world class data systems, and apply economic theory to solve business problems and improve decision science. You will work with business partners to communicate the intuition, implication and detail of your analysis/modeling and be able to incorporate their feedback into your projects.
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. Economists at Amazon are expected to develop new techniques to process large data sets, address quantitative problems, and contribute to design of automated systems around the company.
Other specific tasks:
· Build econometric and machine learning models to answer challenging, impactful questions.
· Partner with other scientists in WW Operations HR to develop scalable solutions to economics problems.
· Dig deep into problems facing employee experience to improve decisions science for large scale issue, such as cost impacts of higher attrition.
· Work with with machine learning scientists to estimate and validate their models on large scale data.
· PhD in Economics, Quantitative, Finance, or closely related field.
· 2+ years of experience in industry, consulting, government or academic research
· Comfortable communicating with technical and non-technical audiences.
Research or work experience in applied microeconomics .
· Applicants with considerably more experience, including mid-career, are also strongly encouraged.
· Strong background in statistics methodology, applications to business problems, and/or big data.
· Ability to work in a fast-paced business environment.
· Strong research track record.