Senior Applied Scientist, Shopping Convo Foundations - Pre-purchases Science
Amazon (AI roles) · Seattle, Washington, USA
No salary listed. Estimated from 344 salary-disclosed Research roles on this board: $225K–$340K (interquartile range; estimate, not the employer's figure).
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The Shopping Convo Foundations Team - Pre-purchases Science is looking for an Senior Applied Scientist with expertise in Artificial Intelligence and Machine Learning to drive scientific innovation that expands Amazon's product catalogue. Our goal is to leverage AI/ML solutions to enhance catalogue coverage with high precision.
In this role, you will lead the research and development of novel machine learning approaches to solve complex catalogue expansion and product attribute challenges. You lead the design and develop state-of-the-art ML models, conduct rigorous experimentation, translate scientific breakthroughs into production-ready solutions, and guide a set of junior scientists. You will work closely with ML Engineers and Software Development Engineers to optimize model performance, ensure scalability, and deploy low-latency solutions at Amazon scale.
About the team
Our team is a horizontal applied science group that works across the full lifecycle of brand and product data extraction and quality. We span seven workstreams — from products sourcing and Brand entitlement, to designing and evaluating extraction strategies for ASINs, offers, and brand attributes at scale, as well as relevance modeling and search. We drive root cause analysis through human-in-the-loop evaluation, improve how catalog data surfaces in search, optimize business metrics tied to data quality, and build brand intelligence capabilities. This cross-cutting scope positions the team as a connective layer across product, engineering, and science — ensuring that improvements in one area compound across the system rather than remain siloed. - 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
In this role, you will lead the research and development of novel machine learning approaches to solve complex catalogue expansion and product attribute challenges. You lead the design and develop state-of-the-art ML models, conduct rigorous experimentation, translate scientific breakthroughs into production-ready solutions, and guide a set of junior scientists. You will work closely with ML Engineers and Software Development Engineers to optimize model performance, ensure scalability, and deploy low-latency solutions at Amazon scale.
About the team
Our team is a horizontal applied science group that works across the full lifecycle of brand and product data extraction and quality. We span seven workstreams — from products sourcing and Brand entitlement, to designing and evaluating extraction strategies for ASINs, offers, and brand attributes at scale, as well as relevance modeling and search. We drive root cause analysis through human-in-the-loop evaluation, improve how catalog data surfaces in search, optimize business metrics tied to data quality, and build brand intelligence capabilities. This cross-cutting scope positions the team as a connective layer across product, engineering, and science — ensuring that improvements in one area compound across the system rather than remain siloed. - 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
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