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# Sr Applied Scientist, Core Shopping Data Science

- **Company:** Amazon.com, Inc.
- **Location:** Seattle, WA, United States
- **Experience:** Expert
- **Salary:** $167,100.0 - $226,100.0
- **Contract:** Permanent contract
- **Skills:** Mxnet, Java (Programming Language), Business Software, C++ (Programming Language), Distributed Systems, R (Programming Language), Apache Hadoop, Python (Programming Language), Machine Learning, NumPy, Software Tools, Tensorflow, SciPy, Data Logging, Large Language Models, Apache Spark, Deep Learning, Spark Mllib, Scikit Learn
- **Published:** August 2, 2026
- **Apply:** https://www.juju.com/job/00000000gl05wg

## About the Role

3+ years of building machine learning models for business application experience

 - PhD, or Master's degree and 6+ years of applied research experience

 - Experience programming in Java, C++, Python or related language

 - Experience with neural deep learning methods and machine learning

Preferred Qualifications

 - Experience with modeling tools such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy etc.

 - Experience with large scale distributed systems such as Hadoop, Spark etc.

## Description

You will model how customers respond to the defects they encounter. Using newly instrumented logging data, you will build representations of customer sessions from page sequences, intent signals, and quality exposures, and quantify what changes downstream when a customer meets an irrelevant recommendation, a set of near-duplicate widgets, or a confusing label early in a journey. You will identify the contextual factors that mediate that impact, including session intent, category, device, and prior interactions, and turn the results into a ranking of which defects are worth trading engagement to prevent, on which surfaces, for which customers.

 - You will distill LLM quality judgments into models compact enough to serve online. That means training compact models against LLM-generated labels, characterizing where the student diverges from its teacher and on which segments, and holding accuracy under the latency and cost budgets of Search, Homepage, and Detail Page ranking. Where a distilled model cannot meet that bar, you will say so early and propose what would.

 - You will work with Search, Homepage, and Detail Page ranking teams to get those signals into online objectives. You will analyze which of those systems offers the most leverage, recommend where to invest first, and design quality-aware objective formulations that trade impression quality against engagement deliberately, replacing the current pattern of suspending a strategy after a problem surfaces.

 - You will prove all of it in controlled experiments. You will design the experiment, choose the right success metrics metrics and make the call on what ships.


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