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# Applied Scientist II, Core Shopping Data Science

- **Company:** Amazon.com, Inc.
- **Location:** Seattle, WA, United States
- **Experience:** Experienced
- **Salary:** $142,800.0 - $193,200.0
- **Contract:** Permanent contract
- **Skills:** Java (Programming Language), C++ (Programming Language), Data Mining, Distributed Systems, Python (Programming Language), Parsing, Software Engineering, High Performance Computing, Large Language Models, Software Version Control
- **Published:** August 4, 2026
- **Apply:** https://www.amazon.jobs/en/jobs/10490800/applied-scientist-ii-core-shopping-data-science

## About the Role

PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience

 - Experience programming in Java, C++, Python or related language
 - Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing

Preferred Qualifications

 - Experience using Unix/Linux
 - Experience in professional software development

## Description

Some CX shopping defects might be straightforward to detect and track. The interesting ones are not because they depend on what a customer perceives. For example, a search page may return legitimately different results, yet a shopper has no way to tell apart. We turn these ambiguous perception questions into a measurable artifact using LLMs, and we build the frameworks to know exactly where model judgment can be trusted and where a human must decide and proving it, against ground truth, at Amazon scale., Design and improve LLM-based labeling for perception-driven defects: prompt design, sampling strategy, and the split between model judgment and human annotation.

 - Validate labeling quality against human ground truth, and build and maintain the golden datasets that make that validation possible.
 - Extend perceived-duplicate measurement to other parts of the shopping experience, designing the methodology where none exists and evaluating approaches already in use where one does.
 - Build reusable, production-grade labeling and evaluation tooling, batch inference, quality sampling, prompt and model version control, that operates on Amazon-scale data.
 - Define and publish the standards other teams adopt for using LLMs to measure customer experience.
 - Partner with science, engineering, and product teams across Stores to make quality measurement usable in their decisions, and present metric results and methodology changes to stakeholders.

A day in the life
We are a small team, which means your work is visible and your scope grows as fast as you do. There are existing partnerships with other teams and a paved way to cross org influence.


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