反作弊工程师（北京）

主导搭建并迭代全业务线风控与反作弊体系，设计多场景策略框架，保障数据真实和公司利益 分析爬虫、虚假交易、账号作弊、刷量薅羊毛等风险特征，输出并落地风控策略，持续优化拦截率与误判率 搭建实时风险监测体系，及时发现新型作弊手段，主导重大风险应急处置，降低业务损失 协同研发、运营等跨团队推动风控工具/引擎落地，输出风控知识，提升团队风险防控意识 ●

计算机、统计学、数学等相关专业，本科及以上学历，3 年及以上风控反作弊经验 扎实数据分析能力，熟练使用 SQL 进行数据查询与清洗，精通 Python（Pandas、NumPy、Scikit-learn 等库）开展特征工程与模型开发 熟悉风控规则引擎（如 URule、Drools）的使用与配置，能将业务规则转化为可落地的风控策略 熟悉 CDN/WAF、设备指纹、浏览器指纹等反爬技术及对抗策略，了解常见爬虫攻击原理与防御逻辑 熟练使用逻辑回归、决策树、随机森林、XGBoost/LightGBM 等风控常用模型，理解模型训练、评估与线上推理全流程 具备敏锐的风险洞察力与逻辑分析能力，优秀的跨团队协作与项目推动能力，能够在高压环境下快速响应并解决复杂风险问题 ● 加分项（选填） 有社区、订阅场景反作弊经验优先 Job Summary We are seeking a highly data mining and technically proficient Anti-Fraud Engineer to join our Risk Control team. In this role, you will be at the forefront of our defense system, designing scalable frameworks to combat sophisticated threats such as bot attacks, fraudulent transactions, and incentive abuse. You will leverage data science, rule engines, and adversarial technologies to safeguard our ecosy Job Responsibilities System Architecture: Lead the end-to-end design and iteration of the company-wide risk control and anti-fraud framework. Architect multi-scenario strategies to safeguard data integrity and corporate interests. Risk Mitigation: Analyze behavioral signatures of risks such as web crawlers, fraudulent transactions, account abuse, and incentive scalping. Develop and deploy targeted strategies to continuously optimize detection precision and minimize false positive/negative rates. Real-time Monitoring & Response: Build real-time risk monitoring systems to identify emerging fraud patterns. Spearhead emergency response efforts for major risk incidents to minimize business losses. Cross-functional Collaboration: Partner with R&D, Operations, and other cross-functional teams to implement risk engines and tools. Disseminate expertise to enhance overall risk awareness across the organization. Job Requirements Educational Background: Bachelor’s degree or above in Computer Science, Statistics, Mathematics, or a related field, with 3+ years of professional experience in risk control and anti-fraud. Data Proficiency: Strong data analysis skills with proficiency in SQL for data extraction and cleaning. Expert in Python (Pandas, NumPy, Scikit-learn) for advanced feature engineering and model development. Rule Engine Expertise: Familiar with the configuration and deployment of risk rule engines (e.g., URule, Drools), with the ability to translate complex business logic into executable technical strategies. Adversarial Defense: Well-versed in anti-crawling technologies and adversarial countermeasures, including CDN/WAF, device fingerprinting, and browser fingerprinting; deep understanding of crawler attack vectors and defense mechanisms. Machine Learning: Hands-on experience with mainstream risk models (e.g., Logistic Regression, Decision Trees, Random Forest, XGBoost/LightGBM) and a thorough understanding of the full lifecycle: training, evaluation, and online inference. Soft Skills: Acute risk insight and logical reasoning. Excellent cross-team collaboration and project management skills, with the ability to resolve complex issues under high-pressure environments. Preferred Qualifications Direct experience in anti-fraud scenarios for online communities or subscription-based services is highly preferred.