DESCRIPTION

How can we improve the customer experience by tailoring what we display on our pages based on available data? How do we build models that help us innovate in different ways to enhance customer experience? What is the relationship between what customers do on the site vs. what they actually buy? How do we do all of this without asking the customer a single question?

Our team's stated missions is to "grow each customer’s relationship with Amazon by leveraging our deep understanding of them to provide relevant and timely product, program, and content recommendations." Recommendations at Amazon is a way to help customers discover products. Our team strives to better understand how customers shop on Amazon (and elsewhere) and build state of the art machine learning models to streamline customers' shopping experience by showing the right products at the right time. Understanding the complexities of customers' shopping needs and helping them explore the depth and breadth of Amazon's catalog is a challenge we take on every day.

Key job responsibilities
Using Amazon’s large-scale computing resources, you will ask research questions about customer behavior, build state-of-the-art models to optimize the shopping experience, and run these models directly on the retail website. You will participate in the Amazon ML community and mentor Applied Scientists and software development engineers with a strong interest in and knowledge of ML. Your work will directly benefit customers and the retail business and you will measure the impact using scientific tools. We are looking for a passionate, hard-working, and talented Applied Scientist who has experience building mission critical, high volume applications that customers love. You will have an opportunity to make an enormous impact on the design, architecture, and implementation of state-of-the-art products used everyday by people you know.