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Web3 Security Data Analyst
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
What does a data scientist in web3 do?
A data scientist in web3 is a type of data scientist who focuses on working with data related to the development of web-based technologies and applications that are part of the larger web3 ecosystem
This can include working with data from decentralized applications (DApps), blockchain networks, and other types of distributed and decentralized systems
In general, a data scientist in web3 is responsible for using data analysis and machine learning techniques to help organizations and individuals understand, interpret, and make decisions based on the data generated by these systems
Some specific tasks that a data scientist in web3 might be involved in include developing predictive models, conducting research, and creating data visualizations.