Do you want to build world's most trusted brand for product discovery and recommendation? Amazon's Choice team is looking for a talented causal inference Economist to help shape algorithms and user experiences that influence better/faster shopping decisions, foster long-term customer trust, and address complex trade-offs between these objectives. Amazon's Choice highlights billions of products on Amazon across all categories and marketplaces, helping customers quickly find a product they like. It has struck a chord with customers, and is scaling rapidly as a result. Its technology is also used in the user experience of multiple partner teams who rely on the accuracy of its algorithms and the strength of its brand.
As an Economist for Amazon's Choice, you will:
* Leverage big data to understand the drivers of customer response to Amazon’s Choice experiences, across both short term and longer term horizons.
* Develop models to continuously measure the overall incremental value of Amazon's Choice experiences, assess heterogeneous effects, and provide insights on opportunities for growth.
* Use learnings from causal inference models and experiments to optimize Amazon's Choice algorithms and provide partner teams with actionable insights on customer preferences.
* Partner with engineering and product owners to productize measurement and optimization models.
* Partner with engineering and product owners to design and implement experiments to test and launch new features.
* Use economic insights to drive the broader strategic direction and expansion of the program.
* Contribute actively to Amazon's Choice science community and leadership and work cross functionally.
· PhD in Economics or closely related field
· Experience in causal inference modeling and machine learning
· Experience in designing and analyzing customer-facing experiments
· Experience designing and analyzing customer-facing experiments
· Experience in using Python, R, or other programming languages to implement ML models and perform data analysis
· Ability to communicate complex science concepts, methods and results/insights to leadership, business stakeholders and partners in an intuitive and effective way
· Familiarity with a Spark and map-reduce computation
· Experience with recommendation algorithms and/or machine learning methods