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株式会社エウレカ - Staff Machine Learning Engineer
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Nagoya University
Doctor of Information Science, Computer and Information Sciences
January 1, 2001 – January 1, 2010
Eureka, Inc.
Software Engineer
May 1, 2020 – Present
Mercari, Inc
Senior Applied and Data Scientist (Software Engineer)
June 1, 2017 – April 1, 2020
日本 東京都 23 区内
Uzabase Inc.
Machine Learning Engineer
August 1, 2016 – May 1, 2017
Emotion Intelligence (AI Startup)
Full Stack Machine Learning Engineer (Player and Manager)
August 1, 2015 – July 1, 2016
Tokyo, Japan
Recruit Communications, Co., Ltd.
Data Mining Enginner / Data Scientist
March 1, 2014 – July 1, 2015
Tokyo, Japan
Rakuten
Application Engineer, Data Mining Engineer, Data Scientist
April 1, 2010 – February 1, 2014
Tokyo, Japan
Cultural Fit Analysis
The candidate has worked in various companies, from startups to large enterprises (Eureka, Mercari, Uzabase, Emotion Intelligence, Recruit, Rakuten), indicating adaptability to different organizational cultures. The roles consistently involve ML/Data Science, aligning well with an ML Engineer target role. The breadth of experience across different domains (e-commerce, financial data, advertising) suggests a diverse skill set and ability to tackle varied challenges. However, the lack of specific project details beyond role descriptions makes it difficult to fully assess cultural fit beyond technical alignment.
Soft Skills & Operational Fit
The candidate's resume indicates experience in managing a data team and full-stack development, suggesting strong ownership and ability to work across different layers of a system. The descriptions highlight problem-solving and innovation. However, without psychometric or English test results, a detailed assessment of communication, logical reasoning, work attitude, stress handling, and team collaboration is not possible.