Exciting opportunity to join the team that owns the science behind AWS HR products, programs, and metrics. We come up with innovative ways to use econometrics, ML, and economic theory to improve the experience of AWS employees and to help HR/recruiters put the right people in the right place at the right time to staff all of AWS.
· Build state of the art models to predict employee headcount and movements across all Amazon businesses
· Build models to understand drivers of promotion, attrition, transfers, and hires
· Identify and work with external and internal talent data-sets and develop ways to combine the two to answer important talent research questions
· Work with other scientists, Data Engineers, and SDEs on the team to scale solutions that have a strong business impact
· Suggest experiments and mechanisms to quantify the impact of policy changes on talent movement
· Work with AWS HR scientists and leadership to develop research roadmap; identify and pitch new opportunities to leadership that are suggested by the data
· PhD in Economics, Statistics, Finance, Machine Learning, or Operations Research
· Four years of experience in private sector or consulting
· Strong proficiency in at least one of the following statistical software packages: Python, Stata, Matlab, R
· Ability to work in a fast-paced business environment.
· Effective verbal and written communications skills.
· Applicants with considerably more experience, including mid-career, are also strongly encouraged.
· Strong research track record.
· Strong background in econometrics (e.g., forecasting, time series, panel data, program evaluation, and/or high dimensional problems), probability and statistics, economic theory, and quantitative methods
· Deep training in theoretical time series along with applied time series experience
· Meets/exceeds Amazon’s leadership principles requirements for this role
· Meets/exceeds Amazon’s functional/technical depth and complexity for this role
Amazon is an Equal Opportunity Employer – Minority / Women / Disability / Veteran / Gender Identity / Sexual Orientation / Age.