About the job
Amazon's Price Perception and Evaluation team is seeking a driven Applied Scientist to harness planet scale multi-modal datasets, and navigate a continuously evolving competitor landscape, in order to build and scale an advanced self-learning scientific price estimation and product understanding system, regularly generating fresh customer-relevant prices on billions of Amazon and Third Party Seller products worldwide. The Applied Scientist will work closely with other research scientists, machine learning experts, and economists to design and run experiments, research new algorithms, and find new ways to improve Seller Pricing to optimize the Customer experience. The Scientist will partner with technology and product leaders to solve business and technology problems using scientific approaches to build new services that surprise and delight our customers.
Responsibilities
Research and use of statistical techniques to create scalable solutions for business problems.
Design, build, and deploy effective and innovative ML solutions to provide low prices and increased selection for customers using scientifically-based methods and decision making.
Evaluate the proposed solutions via offline benchmark tests as well as online A/B tests in production.
Establish scalable, efficient, automated processes for large scale data analyses, model development, validation and implementation.
Publish and present your work at internal and external scientific venues.
Qualifications
Minimum
3+ years of building models for business application experience
PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience
Experience in patents or publications at top-tier peer-reviewed conferences or journals
Experience programming in Java, C++, Python or related language
Preferred
Experience building machine learning models or developing algorithms for business application
Experience applying theoretical models in an applied environment
Bachelor's degree or above in Science, Technology, Engineering, or Mathematics (STEM), or experience in defining and creating benchmarks for assessing GenAI model performance
PhD in econometrics, statistics, industrial engineering, operations research, optimization, data mining, analytics, or equivalent quantitative field, or experience in defining and creating benchmarks for assessing GenAI model performance