Responsibilities
- Analyze large-scale on-chain data to identify anomalous transactions and develop robust detection mechanisms
- Enhance transaction detection capabilities across networks and refine risk monitoring frameworks
- Use community discovery algorithms to identify associated malicious accounts and provide actionable intelligence
- Design a comprehensive data prevention and control system integrating on-chain and off-chain data sources
- Utilize supervised and unsupervised machine learning techniques to identify anomalous patterns (e.g. irregular transaction volumes, unusual wallet behaviors, atypical token movements) across decentralized networks
Requirements
- Over 5 years of experience in blockchain, anti-fraud, risk control, or related fields, strong analytical skills and attention to detail
- Proficient in SQL and experienced in using Python and Spark/Flink; solid understanding of common data mining and machine learning algorithms
- Strong problem-solving and goal-oriented mindset with a proactive learning attitude; keen interest in Web3 security
- Ability to work collaboratively in a fast-paced, dynamic environment
- Fluency in English is required to be able to coordinate with overseas partners and stakeholders
- Knowledge of blockchain and familiarity with on-chain data are considered strong pluses