As a Senior Applied Scientist in the Product Intelligence team, you will lead the development of advanced science solutions to better represent, model, predict and understand the inter-relation between product features, price signals, and price competitiveness.
Your work will leverage the latest in Deep Learning, Active Learning, and Reinforcement Learning to enhance product understanding, and develop advanced explainability capabilities and price reasoning methods for a deeper understanding of products and pricing competitiveness.
You will also lead research projects to tackle pricing-related unsolved problems, mentor interns, and author academic papers to summarize your findings for external publication.
This high-impact role is critical to our core business, influencing the reliability of information for billions of products on Amazon's platform, and impacting the shopping journey for hundreds of millions of customers. The systems you build will be used to monitor Amazon's entire product selection to ensure their quality, the availability of accurate price distributions, and more.
We seek an experienced scientist with deep knowledge of ML, DL, RL, and GenAI. The role will also require cross-functional collaboration skills, and staying up to date with the latest advancements in Generative AI, model explainability, quantile-regression models, etc.
Key job responsibilities
Implement and deploy systems that leverage ML, DL, RL and other techniques to address some of hardest problems in the pricing space.
Set scientific standards and see the big picture to influence Amazon's long-term vision for retail pricing science.
Work cross-functionally with various teams to align machine learning initiatives with business goals and execute them successfully.
Lead research projects and participate in the publication of external academic papers at top conferences and journals.
About the team
Within Pricing & Promotions Science, the Product Intelligence team leverages billion-scale multi-modal data on billions of Amazon and external competitor products to build advanced machine learning models for product similarity, substitutability, error detection & correction, and probabilistic price estimation. We preserve long term customer trust by ensuring Amazon's prices are always competitive and error free.
