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 Applied 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
* PhD in a quantitative field (e.g., computer science, machine learning, mathematics, statistics) or Master’s degree with equivalent experience
* 3+ years’ experience with building machine learning models for complex business applications
* Extensive knowledge and practical experience in several of the following areas: machine learning, deep learning, statistics, NLP, recommendation system, signal processing.
* Experience with Python, R, SAS, Matlab or other statistical/machine learning software
* Experience in efficiently handling large data sets, e.g., by using SQL, and databases in a business environment
* Experience developing experimental and analytic plans for data modelling processes, use of strong baselines and the ability to accurately determine cause and effect relationships
* Ability to clearly communicate insights, in verbal and written form, to stakeholders (scientists, engineering teams and business audiences) and senior management
* Strong troubleshooting and problem solving skills
* Experience working with language data, speech recognition and/or natural language processing (NLP); Good understanding of state-of-art NLP models (bi-LSTM, BERT, RoBERTa, GTP-2/3 etc.)
* Experience in writing academic-style papers for presenting both the methodologies used and results for data science projects
* Preferred but not necessary: Native or near-native fluency in German and/or French
* Strong attention to detail and experience balancing multiple tasks and deadlines
Amazon is an equal opportunities employer. We believe passionately that employing a diverse workforce is central to our success. We make recruiting decisions based on your experience and skills. We value your passion to discover, invent, simplify and build. Protecting your privacy and the security of your data is a longstanding top priority for Amazon. Please consult our Privacy Notice (https://www.amazon.jobs/en/privacy_page) to know more about how we collect, use and transfer the personal data of our candidates.