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Principal Scientist - Machine Learning & NLP at Amazon
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Assessing your cultural and operational fit
Publications: https://scholar.google.com/citations?user=rk587vcAAAAJ&hl=en Generated text: Experienced Machine Learning Researcher with a demonstrated history of working in the internet industry. Skilled in Computational Linguistics, Computer Science, Computer Vision, Artificial Intelligence, and Semantics. Strong professional with a Doctor of Philosophy (Ph.D.) focused in Computer Science, Computational Linguistics from University of Szeged.
University of Szeged
Doctor of Philosophy (Ph.D.), Computer Science, Computational Linguistics
January 1, 2004 – January 1, 2008
Szegedi Tudományegyetem
Master of Science (MSc), Computer Science
January 1, 1998 – January 1, 2003
Amazon Web Services (AWS)
Principal Scientist
September 1, 2024 – Present
Berlin, Germany · On-site
Amazon
Principal Scientist
October 1, 2023 – September 1, 2024
Amazon
Applied Science Manager / ML & NLP
January 1, 2019 – Present
Amazon
Sr. Machine Learning & NLP Scientist
May 1, 2016 – September 1, 2023
Amazon
Machine Learning & NLP Scientist
July 1, 2013 – April 1, 2016
Nuance Communications
Research Engineer in NLP
October 1, 2012 – June 1, 2013
Kreisfreie Stadt Aachen Area, Germany · On-site
Technical University Darmstadt
Post-Doctoral Researcher
March 1, 2009 – September 1, 2012
University of Szeged, Human Language Technology Group
research fellow
November 1, 2008 – January 1, 2013
Hungarian WordNet (Magyar WordNet)
January 1, 2005 – January 1, 2007
Hungarian Wordnet (HuWN): a wordnet ontology for Hungarian, developed by the University of Szeged, Research Institute for Linguistics, HAS and Morphologic Ltd. HuWN covers 42,000 synsets. It follows the grounds of the EuroNet and BalkaNet projects: contains localisation of the BalkaNet Concept Set synsets and is fully aligned with Princeton WordNet 2.0. In addition to the standard semantic relations in WN, it introduces some novel relations to cover the special characteristics of Hungarian.
Cultural Fit Analysis
The candidate's long tenure and progression within Amazon, a large, fast-paced, and innovation-driven company, indicates a strong cultural fit for similar environments. Their involvement in both research and applied science roles, along with leading teams, suggests adaptability and a collaborative mindset. The personal project 'Hungarian WordNet' demonstrates a deep academic interest and commitment to the field beyond corporate roles. The target role of 'NLP Engineer' aligns perfectly with their extensive experience and academic background.
Soft Skills & Operational Fit
The candidate's extensive experience at Amazon, progressing to Principal Scientist and Applied Science Manager roles, suggests strong leadership, problem-solving, and operational skills in a demanding environment. Their work on conversational AI and agentic AI indicates an ability to handle complex, cutting-edge projects. The lack of psychometric test results prevents a direct assessment of logical reasoning, work attitude, stress handling, and team collaboration, but their career progression implies strong capabilities in these areas.