1. Adopt statistical methods and machine learning for data analysis, evaluation of user needs and user life cycle value. Develop models and use data throughout the entire life cycle management.
2. Establish and deploy industry-leading machine learning models.
3. Participate in the R&D of data products and their applications; explore the commercial value of data; create ultimate experience for data products; boost user growth; and improve the efficiency and ability of risk management.
4. Conduct basic researches in deep learning, reinforced learning, text image processing, speech recognition, NLP, statistics, AI and other fields.
1. Master’s and doctorate degree in computing science, mathematics, statistics, economics, operational research or other quantitative related fields.
2. Knowledge of common statistics and machine learning algorithms, including but not limited to LR, GBDT, neural network, deep learning, reinforcement learning, NLP, graph embedding, prediction; and abilities to use R, Python or SAS software for analysis and modelling.
3. Experience in data mining and large-scale data processing; and SQL knowledge.
4. Excellent logical thinking, high business sensitivity and data sensitivity; ability to adapt to intensive and fast-paced work; and exemplary writing and oral communication skills.
5. Experience in risk control, strategy, analysis, and modelling (preferred).
6. Ability of quick learning and problem solving; teamwork spirit and cooperation skills.