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Economist II, Catalog Experimentation

Job ID: 2191686 | Amazon.com Services LLC

DESCRIPTION

Job summary
If you are an economist, who dreams of conceptualizing an Amazon wide program from scratch and possess the confidence to navigate through early-stage ambiguities, read on.

Amazon’s customers rely on the quality of Amazon’s product data to make well-informed purchase decisions. Improving the quality of product data is a continuous process. Amazon Selection Catalog Systems (ASCS) is building Catalog Experimentation Platform (CEP) to enable teams across Amazon to make data driven decisions through direct, customer-randomized experimentation on what product data changes simplify and improve the Customers’ experience. In 2021, CEP users ran close to 100 experiments, with more expected and larger experiments expected in 2022.

Our team seeks an economist with demonstrated experience conducting causal inference at scale. We will be looking to this economist to work on and propose projects that: (1) help improve the accuracy of CEP experiments (e.g., how to account for spillovers when measuring benefit to Amazon? How to design a solution for extrapolating any experiment to a wider population?), (2) increase the depth of learnings from a single experiment (e.g., automated methods for experiment deep dive). To achieve this, the economist will combine causal modelling, machine learning and big data. This is an opportunity to influence catalog quality improvements across Amazon across hundreds of initiatives

Key job responsibilities
As an Economist, your responsibilities will be: (1) develop science products for CEP users, (2) contribute to CEP's science roadmap, (3) support ML scientists and engineers on causal related topics, (4) design experiments for high impact, complex use cases, (5) bar raise CEP experiments

About the team
We are a fast growing team in Amazon Selection Catalog Systems. We have proven the concept of experimenting with catalog product data attributes with the help of a small hard working and passionate team. Now, we have the license to expand our charter through rapid growth.

BASIC QUALIFICATIONS

  • PhD in Economics or closely related field

1. Hands on experience working in the causal inference domain.
2. Keen judgment for balancing research depth and pacing
3. Work and collaborate effectively with tech teams, and launch to production.
4. Influence senior leadership teams on the value propositions and generate new product ideas.

PREFERRED QUALIFICATIONS

1. Knowledge of machine-learning techniques applied to causal inference
2. Demonstrated skills implementing and deploying large scale machine learning applications and tools.
3. Proficiency with a scripting language such as R, Matlab, Python

Amazon is an Equal Opportunity-Affirmative Action Employer – Minority / Female / Disability / Veteran / Gender Identity / Sexual Orientation





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