Data Science Intern

Full time

Employment Information

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:

  • 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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