Sr Applied Scientist, Sponsored Products and Brands Ads Response Prediction

Amazon
Palo Alto, CA, USA / Seattle, WA, USA2026-04-07ONSITE

About the job

The Sponsored Products and Brands team at Amazon Ads is re-imagining the advertising landscape through industry leading generative AI technologies, revolutionizing how millions of customers discover products and engage with brands across Amazon.com and beyond. We are at the forefront of re-inventing advertising experiences, bridging human creativity with artificial intelligence to transform every aspect of the advertising lifecycle from ad creation and optimization to performance analysis and customer insights. We are a passionate group of innovators dedicated to developing responsible and intelligent AI technologies that balance the needs of advertisers, enhance the shopping experience, and strengthen the marketplace. If you're energized by solving complex challenges and pushing the boundaries of what's possible with AI, join us in shaping the future of advertising.

Responsibilities

Conduct deep data analysis to derive insights to the business, and identify gaps and new opportunities

Develop scalable and effective machine-learning models and optimization strategies to solve business problems

Run regular A/B experiments, gather data, and perform statistical analysis

Work closely with software engineers to deliver end-to-end solutions into production

Improve the scalability, efficiency and automation of large-scale data analytics, model training, deployment and serving

Conduct research on new machine-learning modeling to optimize all aspects of Sponsored Products and Brands business

Qualifications

Minimum

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

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.