Applied Scientist, Worldwide Returns & Recommerce - Science

Welcome to the Worldwide Returns & ReCommerce team (WWR&R) at Amazon.com. WWR&R is an agile, innovative organization dedicated to ‘making zero happen’ to benefit our customers, our company, and the environment. Our goal is to achieve the three zeroes: zero cost of returns, zero waste, and zero defects. We do this by developing groundbreaking products and driving truly innovative operational excellence to help customers keep what they buy, recover returned and damaged product value, keep thousands of tons of waste from landfills, and create the best customer returns experience in the world. We have an eye to the future – we create long-term value at Amazon by focusing not just on the bottom line, but on the planet. We are building the most sustainable re-use channel we can by driving multiple aspects of the Circular Economy for Amazon – Returns & ReCommerce.

Amazon WWR&R is comprised of business, product, operational, program, software engineering and data teams that manage the life of a returned or damaged product from a customer to the warehouse and on to its next best use. Our work is broad and deep: we train machine learning models to automate routing and find signals to optimize re-use; we invent new channels to give products a second life; we develop highly respected product support to help customers love what they buy; we pilot smarter product evaluations; we work from the customer backward to find ways to make the return experience remarkably delightful and easy; and we do it all while scrutinizing our business with laser focus.

You will help create everything from customer-facing and vendor-facing websites to the internal software and tools behind the reverse-logistics process. You can develop scalable, high-availability solutions to solve complex and broad business problems.

Design, develop, and evaluate highly innovative models for our Natural Language Understanding (NLU) and Large Language Model (LLM) models

Use SQL to query and analyze the data

Use Python, Jupyter notebook, and Pytorch to train/test/deploy NLU/LLM/GenAI models

Use machine learning and analytical techniques to create scalable solutions for business problems

Research and implement novel machine learning and statistical approaches

Mentor interns

Work closely with data & software engineering teams to build model implementations and integrate successful models and algorithms in production systems at very large scale