Applied Scientist, Delivery Experience AI Team
Amazon
DESCRIPTION
Why this job is awesome?
- This is SUPER high-visibility work: Our mission is to provide consistent, accurate, and relevant delivery information to every single page on every Amazon-owned site.
- MILLIONS of customers will be impacted by your contributions: The changes we make directly impact the customer experience on every Amazon site. This is a great position for someone who likes to leverage Machine learning technologies to solve the real customer problems, and also wants to see and measure their direct impact on customers.
- We are a cross-functional team that owns the ENTIRE delivery experience for customers: From the business requirements to the technical systems that allow us to directly affect the on-site experience from a central service, business and technical team members are integrated so everyone is involved through the entire development process.
- Do you want to join an innovative team of scientists and engineers who use machine learning and statistical techniques to deliver the best delivery experience on every Amazon-owned site?
- Are you excited by the prospect of analyzing and modeling terabytes of data on the cloud and create state-of-art algorithms to solve real world problems?
- Do you like to own end-to-end business problems/metrics and directly impact the profitability of the company?
- Do you like to innovate and simplify?
If yes, then you may be a great fit to join the Delivery Experience Machine Learning team.
Key job responsibilities
· Work closely with business teams to identify business and science opportunities for providing the best delivery experience on all Amazon-owned sites
· Analyze a large amount of historical data to detect patterns, analyze trends and identify correlations and causalities
· Innovate, design, develop and evaluate machine learning models to solve business problems and improve customer experience
· Work closely with software engineering teams to establish automated, efficient and scalable processes for data collection, retention and analysis as well as model development, validation, deployment and maintenance