Shopee's growth data team focuses on the long-term performance of users and is committed to associating users' long-term performance with short-term behaviour through scientific methods. Our team plays a critical role in improving business framework, evaluation system and customisation strategies. Data-driven and result-oriented are our values; helping business to achieve long-term goals is our mission. We always run fast with challenges and remain at the forefront of the industry.

Job Description:

Including but not limited to:
  • Responsible for user CLV prediction, establishing real-time prediction model, and responsible for basic algorithm and strategy research of CLV system in multiple scenarios.
  • Responsible for the feature engineering of algorithms, continue to expand business tags, mine user portraits, and continuously improve prediction accuracy.
  • Build and optimise the algorithm architecture, improve forecasting efficiency, and develop data products and decision-making tools based on business scenarios.

Requirements:

  • Bachelor degree or above in computer or related majors, more than 1 year of Internet work experience.
  • Familiar with at least one programming language such as Python/Java/C++/Scala, and familiar with common big data processing frameworks such as Spark/Hadoop.
  • Solid machine learning foundation, able to apply commonly-used machine learning models to solve practical business problems proficiently, project experience with mainstream deep learning models is preferred.
  • Good logical thinking, data analysis ability, good at analysing and solving problems.
  • Advertising/recommendation/search work experience is preferred.

 

工作职责

包括但不限于:

  • 负责用户CLV的预测,建立实时预测模型,负责多场景下CLV系统的基础算法和策略研究。
  • 负责算法的特征工程,持续拓展业务标签、挖掘用户画像,不断提升预测精度。
  • 搭建并优化算法架构,提升预测效率,结合业务场景开发数据产品及决策工具。

 

任职要求

  • 计算机或相关专业本科及以上学历,1年以上的互联网工作经历。
  • 熟练掌握python/java/C++/Scala等一门或以上的编程语言,熟练掌握spark/hadoop等常用大数据处理框架。
  • 扎实的机器学习基础,能够熟练应用常用的机器学习模型解决实际的业务问题,有主流深度学习模型的项目实践经验优先。
  • 良好的逻辑思维能力, 数据分析能力,善于分析和解决问题。
  • 广告/推荐/搜索的工作经历优先。

 

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