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# Sr. Applied Scientist, Amazon Ads

- **Company:** Amazon.com, Inc.
- **Location:** San Francisco, CA, United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Mxnet, Java (Programming Language), Data Analysis, Big Data, Business Software, C++ (Programming Language), Distributed Systems, R (Programming Language), Apache Hadoop, Python (Programming Language), Machine Learning, NumPy, Software Tools, Tensorflow, SciPy, Large Language Models, Apache Spark, Deep Learning, Spark Mllib, Scikit Learn
- **Published:** July 22, 2026
- **Apply:** https://www.amazon.jobs/en/jobs/10480719/sr-applied-scientist-amazon-ads

## About the Role

5+ 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

As a Senior Applied Scientist in Amazon Ads, you will:

 - Conduct hands-on data analysis, build large-scale machine-learning models and pipelines
 - Build ML models, perform proof-of-concept, experiment, optimize, and deploy your models into production, working closely with cross-functional teams including engineers, product managers, and other scientists
 - Design and run A/B experiments to validate hypotheses, gather insights from large-scale data analysis, and measure business impact.
 - Research and implement ML approaches, including applications of generative AI and large language models.
 - Drive end-to-end optimization projects that tackle ambiguous problems at massive scale, often working with petabytes of data.
 - Provide technical leadership, research new machine learning approaches to drive continued scientific innovation
 - Build supply optimization models, perform proof-of-concept, experiment, optimize, and deploy your models into production; work closely with software engineers to assist in productionizing your ML models.
 - Develop scalable, efficient processes for model development, validation, and deployment that optimize traffic monetization while maintaining customer experience


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