As a lead in e-Commerce, Amazon is building the authoritative knowledge base for every product in the world. With hundreds of millions of customers and billions of products, Amazon will offer a challenging but fun journey to turn this big and rapidly changing data into high-quality knowledge, and the great opportunities to impact various aspects of eCommerce. We look for research scientists who love big data, who are passionate about improving quality of data, and who are capable of inventing machine learning and data cleaning techniques that will leave no valuable data behind.
The Product Graph team at Amazon, based in Seattle, has multiple positions for applied scientists with expertise in knowledge management, data cleaning and integration, natural language processing, machine learning, and graph processing. Our applied scientists work closely with software engineers to put algorithms into practice. They also work in partnership with teams across Amazon to create enormous benefits for our customers.
We target at inventing advanced solutions for automatic knowledge collection and knowledge cleaning. Our scientists will survey state-of-the-art techniques to increase coverage and quality of knowledge, conduct extensive empirical comparison of existing solutions, and invent new algorithms or models that push the envelope for product knowledge and for generic knowledge management. The research topics will include, but are not limited to, the following.
· Human-in-the-loop knowledge learning
· Fact and feature extraction from product descriptions and reviews
· Knowledge extraction from the Web combining Open IE and Close IE techniques
· Product trend discovery from web
· Large-scale collective entity resolution
· Quantitative and logical error detection
· Knowledge verification from external sources
· Leveraging structured knowledge in deep learning
· Personalization using personal knowledge base