Applied Scientist, Pricing Science
Amazon · Seattle, WA · Applied Science
About this role
Amazon is hiring a mid-level Applied Scientist in the machine learning function based in Seattle, WA. The posting calls out experience with Python, Java, Reinforcement Learning, Distributed Systems. Listed education preference: a bachelor's degree or equivalent. Compensation is listed at $142,800–$193,200 per year.
- Role
- Applied Scientist
- Function
- machine learning
- Level
- mid
- Track
- Individual contributor
- Employment
- Full-time
- Location
- Seattle, WA
- Education
- Bachelor's degree
- Department
- Applied Science
- Posted
- Mar 2, 2026
More roles at Amazon
Job description
from Amazon careersAmazon's Pricing Science is seeking a driven Applied Scientist to harness planet scale multi-modal datasets, and navigate a continuously evolving competitor landscape, in order to regularly generate fresh customer-relevant prices on billions of Amazon products worldwide. We are looking for a talented, organized, and customer-focused applied researchers to join our Pricing Optimization science group, with a charter to measure, refine, and launch customer-obsessed improvements to our pricing algorithms across all products listed on Amazon. This role requires an individual with exceptional machine learning and predictive modeling skills, causal and experimental evaluation experience, excellent cross-functional collaboration skills and business acumen, and an entrepreneurial spirit. We are looking for an experienced innovator, who is a self-starter, comfortable with ambiguity, demonstrates strong attention to detail, and has the ability to work independently to deliver business impact. Key job responsibilities - See the big picture. Understand and develop science to influence the long term vision for Amazon's science-based competitive, perception-preserving pricing techniques - Build strong collaborations. Partner with product, engineering, and data teams within Pricing Promotions to deploy models at Amazon scale - Stay informed. Establish mechanisms to stay up to date on latest scientific advancements in machine learning, reinforcement learning, causal ML, and…