to mine massive datasets and identify various account security issues, including but not limited to fake registrations, account takeovers, account trading, account nurturing, and session hijacking.
- Analyze and uncover hidden data correlations to build an account risk perception framework, enabling early detection and rapid response to emerging risks.
- Contribute to the development of account risk control infrastructure, including data warehouses, feature engineering, and machine learning models.
Qualifications
Minimum Qualifications:
- Master's degree or above in Computer Science, Statistics, Artificial Intelligence, or a related field.
- Solid engineering skills. Proficiency in one or more programming languages such as C++, Java, or Python.
- Familiarity with big data technologies such as SQL/HQL, and hands-on experience with one of Hadoop, Hive, Spark, or Flink.
- Solid understanding of machine learning, deep learning, and large language models; knowledge of clustering, community detection, and related techniques, with the ability to validate algorithm effectiveness and apply them to risk control scenarios.
Preferred Qualifications:
- Prior experience in combating online fraud, abuse, or anti-cheating in the internet industry .
- Familiarity with emerging technologies such as large language models (LLMs) and AI Agents.