Responsibilities
1. Comprehensive use of data mining, graph association, behavior sequence analysis, logical reasoning and other methods to restore Internet risk event links (account security, transaction fraud, content security, marketing cheating, crawler confrontation, etc.), mine risk behavior patterns, locate risk subjects, cheating links and key data characteristics 2. Mining data characteristics based on risk data analysis and external investigation conclusions, designing reusable and implementable data products, and collaborating with the algorithm & engineering team to promote the productization and platformization of risk control data capabilities 3. In-depth understanding of business models, attack and defense links, and risk control product mechanisms, and promoting the in-depth application and value recovery of risk control data capabilities in business scenarios. Collaborate with strategy, algorithm, engineering, governance and other teams to formulate solutions around key risk issues and promote the implementation of closed-loop risk governance.
Qualifications
1. Information security, computer, data science and other related professional backgrounds, with solid data analysis capabilities, proficiency in SQL, and the ability to independently complete complex data extraction and modeling analysis 2. Have relevant experience in Internet risk control data mining, risk control strategies, black and gray product research, etc., be familiar with Internet business data structure, and be familiar with at least one typical Internet business risk control scenario (such as account security, transaction fraud, content risk, marketing cheating, crawler confrontation, etc.). Applicants with practical experience from risk identification to strategy implementation are preferred 3. Have good product-oriented thinking, excellent cross-team communication and project promotion capabilities, and be able to coordinate multiple resources such as strategies, algorithms, engineering, and governance to promote the reuse and large-scale implementation of data capabilities to achieve business value. Bonus points: 1. Have experience in independently completing or being fully responsible for data products and successfully implementing them 2. Be familiar with the basic principles and use of large language models (LLM) and AI Agents, and have participated in the design and development of Agent tools/workflows in risk control scenarios 3. Have strong understanding of security technologies (Web, client security, system vulnerabilities, privacy protection, etc.), and have experience in mobile reverse engineering, Windows reverse engineering, penetration testing, and Web attack and defense 4. Master a programming language, including but not limited to Python, Go, Java, C/C++, and JavaScript, and have certain development capabilities be familiar with behavioral algorithms, machine learning, or data mining algorithms, and apply them to risk analysis and feature engineering.
