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DESCRIPTION
We are seeking a scientist with rigorous training in A/B testing and observational causal modeling (e.g. synthetic controls, causal matrix completion). Our team owns “causal inference as a service” for the Pricing and Promotions organization; we run A/B tests on new pricing, promotions, and pricing/promotions CX algorithms and, where experimentation is impractical, conduct observational causal studies.
Key job responsibilities
We are seeking a scientist excited to build the next generation of pricing, promotions, and pricing/promotions CX for Amazon. On our team, you will work at the intersection of economic theory, statistical inference, and machine learning to design and implement in production new statistical methods for measuring causal effects of an extensive array of business policies.
This position is perfect for someone who has a deep and broad analytic background, is passionate about using mathematical modeling and statistical analysis to make a real difference. You should be familiar with modern tools for data science and business analysis and have an interest in working with engineers to put projects into production. We are particularly interested in candidates with research background in experimental statistics.
A day in the life
- Discuss business problems with business partners, product managers, and tech leaders
- Brainstorm with other scientists to design the right model for the problem at hand
- Present the results and new ideas for existing or forward looking problems to leadership
- Dive deep into the data
- Build working prototypes of models
- Work with engineers to implement prototypes in production
- Analyze the results and review with partners
About the team
We are a team of scientists who design and implement the econometrics powering pricing, promotions, and pricing/promotions CX.
BASIC QUALIFICATIONS
- PhD in economics or equivalent
- Experience in building statistical models using R, Python, STATA, or a related software
PREFERRED QUALIFICATIONS
- Experience with modern machine-learning methods (e.g., boosted regression trees, random forests, neural networks)
Amazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status.
Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.
Our compensation reflects the cost of labor across several US geographic markets. The base pay for this position ranges from $116,300/year in our lowest geographic market up to $201,000/year in our highest geographic market. Pay is based on a number of factors including market location and may vary depending on job-related knowledge, skills, and experience. Amazon is a total compensation company. Dependent on the position offered, equity, sign-on payments, and other forms of compensation may be provided as part of a total compensation package, in addition to a full range of medical, financial, and/or other benefits. For more information, please visit This position will remain posted until filled. Applicants should apply via our internal or external career site.#J-18808-Ljbffr
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