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T

Tencent

Data Science Intern @ Tencent

location icon New Zealand-Auckland
Full Time, Internship
Responsibilities:Responsibilities: Research on state-of-the-art machine learning, deep learning, social-network data mining and recommendation algorithms with game data, help design, evaluate and improve the core algorithm and strategy for online AI serving. Response for deep statistics analysis from large-scale data, mining for user behavior and attributes, building user portraits. By datamining and analysis, solving user pain point and proposing strategies improving user retention. Improving data analysis pipelines, combining business requirements and data, by using distributed computation platform, develop efficient and reliable data mining solutions. Research on recommendation algorithm or matching algorithm in gaming environments.Requirements:Qualifications: Familiar with basic and latest research result in data mining, with a strong understanding of statistical models, related machine learning principles, and experience working on data mining related projects. Familiar with machine learning, deep learning and recommendation algorithms such as clustering and classification, grasping and having deep understanding in at least one of them. Proficient in at least one programming language, familiar with basic data structures and algorithms. Higher priority for candidates familiar with graph models, including community discovery, graph embedding、influence diffusion models. Higher priority for candidates familiar with causal inference or optimization algotirhms, and have strong Higher priority for candidates having big data experience, and familiar with distributed computation framework such as Hadoop, spark, hive and etc. Higher priority for candidates with strong logical thinking ability, communication ability and business understanding, and familiar with game situations. And candidates with better mathematics background are in favor. Strong self-driven ability, team cooperation ability, anti pressure ability and studious candidates is preferred.
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