Research Scientist, Alexa in Berlin

Job summary
Are you interested in delighting Alexa customers around the world? Come join us and help shape Alexa's future. We are the Alexa Natural Language Understanding (NLU) Non-English eXpansion Team (NEXT) team in Berlin! We are tackling some of the most interesting problems on the leading edge of Machine Learning and NLU for German and French Alexa.

We are seeking a creative Research Scientist who has profound knowledge and experience in machine learning / data science and is passionate about working with language data and model. Our ongoing challenges include various aspects of NLU modelling, such as model accuracy and robustness, efficient model training and testing. You will be responsible for delivering insightful data analytics to optimize accuracy and efficiency of our NLU models, where your work will have a direct impact on our customers. You should have extensive experience in research as well as business analytics and have the aptitude to incorporate new approaches and methodologies while dealing with ambiguities in sourcing processes. You should have a demonstrated ability to think strategically and analytically about business, product and technical challenges. This role requires a strong passion for customers, a high level of comfort navigating through ambiguity and a keen sense of ownership and drive to deliver results.



Key job responsibilities
* Ensure data quality throughout all stages of processing, including such areas as data sourcing/collection, ground truth generation, normalization, transformation, cross-lingual alignment/mapping, etc.
* Create experiments and prototype implementations of new learning algorithms and prediction techniques
* Continuously evaluate data analysis tools and processes and offer solutions to ensure they are efficient, high-quality and scalable
* Lead high impact programs collaborating with various teams across Amazon (from science, engineering and business backgrounds)
* Lead and test tooling developments and pilot processes to support expansion to new data areas
* Present proposals and results in a clear manner backed by data and coupled with actionable conclusions