Вакансии
Senior Researcher/Technical leader in Data Analysis and Data Mining
HUAWEI
Город: | Москва |
З/п: | обсуждается с кандидатом |
Опыт работы: | Более 6 лет |
Отзывы о компании HUAWEI |
Responsibilities:
· To understand massive data in current enterprise business, we need to provide an analytics infrastructure to support Huawei industry solutions.
· The ongoing work is promising as it will serve not only telecom but also other industries including healthcare, smart grid, smart city, etc.
· The researcher will lead the innovation process and designing new massive data analysis algorithm through global team work, make break-through for the coming Huawei’s products and solutions.
We're growing extremely rapidly and welcoming talents who have the following traits:
Skills and Traits Required:
· 8+ years R&D experience in Data Analysis and Data Mining and related areas.
· Data mining and machine learning, deep understanding of related algorithms including numeric prediction, classification, clustering, time series analysis, and instance based learning, etc. The ability to apply, extend and innovate data mining/machine learning algorithms according to different requirements.
· Comprehensive knowledge on massive parallel data analysis methodology, especially Hadoop and Map Reduce mechanism.
· Solid understanding of object oriented software design principles and skills, including UML, Java, c++ and Design Patterns.
· Strong skills in data modeling including data warehouse best practices, including start model and snowflake model.
· A passion for finding elegant solutions to complex problems.
· Excellent verbal and written communication skills in English.
· Excellent interpersonal skills as well as a team player.
Skills and Traits Preferred:
· Experience in Open source and commercial software in reporting, analytics and ETL are preferred.
· Deep knowledge in probability and statistics will be considered as an advantage.
· PhD or above is necessary.
· Graduated from department of computer science or applied mathematics is preferable.
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