Are you passionate about conducting research to improve people’s lives? Would you like to impact more than one million Amazonians globally and improve their employee experience? If so, you should consider joining Organizational Research and Measurement (ORM) for World Wide Consumer Talent. Our goal is to be the best and most diverse workforce in the world. ORM uses science, research and technology to optimize employee experience and performance across the full employee lifecycle, from first contact through exit.
We are looking for outstanding economists to join a new team that focuses on the economic issues of more than one million associates at Amazon. Ideal candidates will work with data scientists and data engineers to frame ambiguous and complex business problems into specific scientific questions, design experiments and develop models using large scale data, and transform the insights into improved policies and products at scale. Ideal candidates should have strong business acumen, practical technical skills in causal inference, and be able to communicate the insights to multiple audiences (e.g., scientific peers, functional teams and business leaders).
· PhD in economics, finance, quantitative marketing, or related field.
· 2+ years of research experience in industry, consulting, government or academia leveraging techniques from economics or related disciplines.
· Proficient with SQL and strong coding ability in a scripting language such as R and python.
· Ability to work in a fast-paced business environment.
· Proven track record in end-to-end research and solution of business problems from data gathering, exploratory data analysis, hypothesis testing, causal inference, impact analysis to model-driven prediction.
· Demonstrable experience in any of the following areas: applied econometrics, labor economics, behavioral economics, or marketing analytics.
· Demonstrable experience in survival analysis, discrete choice models, panel data analysis, predictive modeling, and research design methodologies for experiments and quasi-experiments.
· Strong background in statistics methodology and machine learning techniques.
· Excellent written and verbal communication skills for both technical and non-technical audiences.