Summary
We’ve solved many complex problems to get to where we are today, but there are still plenty of challenges ahead of us, and Alexa is getting smarter every day. The problems we solve in the Alexa Knowledge team in Cambridge help Alexa get smarter by understanding the different ways people talk, by learning more and more facts about the world, by improving her common sense reasoning, and by responding in the most natural way possible in multiple languages.
We set out to build Alexa at Amazon because we believe that voice will fundamentally improve the way people interact with technology, and we’re breaking boundaries by allowing users to access information and services today through voice, with many more exciting projects in the pipeline.
In the Knowledge Extraction and Understanding group, we are constantly making Alexa smarter by enabling her to learn about what’s going on in the world. We use multiple Machine Learning and NLP techniques to enable learning and reasoning across a range of structured and unstructured data. We’re at the forefront of both research and engineering for understanding user demands and extracting the right knowledge from data sources to expand the information Alexa accesses – all to improve Alexa and give customers the best experience. The scope of the teams in the Knowledge Extraction and Understanding group is broad, covering a diverse range of problem spaces including, but not limited to, semantic understanding, structured data extraction, fact extraction and verification from unstructured text, natural language generation, machine translation, model adaptation and large-scale categorisation. What’s more, we do it at scale, bringing all these solutions to the millions of customers who use Alexa every day.
As a Data Scientist in Knowledge Extraction and Understanding you will bring academic and/or industrial experience in data analysis over multiple datasets to surface metrics and visualisations about how users interact with Alexa. You’ll research and help develop systems to solve complex problems measuring the success and failure of question answering in Alexa at web scale. You’ll be encouraged to publish work in top-tier conferences and journals as well as sharing your research within Amazon.
BASIC QUALIFICATIONS
· PhD Degree in Computer Science, Machine Learning, Computational Linguistics, Natural Language Processing, Applied Mathematics or a related field, or equivalent experience
· Post PhD experience
· Strong academic record of refereed publications in top tier conferences or journals
· Hands-on experience in one or more of: Data Analytics, Statistics, Time Series Analysis, Stochastic Modelling, Machine Learning, Information Extraction
· Active member of the research community
· Experience building machine learning models with knowledge of commonly used ML languages (MATLAB, Python, R)
· Excellent communication skills and the ability to work in a team
· Experience mentoring junior scientists
· Track record of leading projects and/or building research agendas
PREFERRED QUALIFICATIONS
· Ability to convey complex mathematical concepts and considerations to non-experts
· Relevant industrial research experience is a plus
· Experience working with large datasets
