Build rules, algorithms and machine learning models, to respond to and mitigate business risks in Tiktok products/platforms. Such risks include and are not limited to account integrity, scapler, deal-hunter, malicious activities, brushing, click-farm, information leakage etc; Analyze business and security data, uncover evolving attack motion, identify weaknesses and opportunities in risk defense solutions, explore new space from the discoveries; Define risk control measurements. Quantify, generalize and monitor risk related business and operational metrics. Align risk teams and their stakeholders on risk control numeric goals, promote impact-oriented, data-driven data science practices for risks
Requirements:
Bachelor or degrees above in computer science, statistics, math, internet security or other relevant STEM majors (e.g. finance if applying for financial fraud roles); At least 5 years with solid data science skills. Proficiency in statistical analytical tools, such as SQL, R and Python; Familiarity with machine learning or social/content online platform analytics. Bonus given to proficiency in modern machine learning applications
Text:
Machine Learning Engineer - TikTok E-Commerce Build rules, algorithms and machine learning models, to respond to and mitigate business risks in Tiktok products/platforms. Such risks include and are not limited to account integrity, scapler, deal-hunter, malicious activities, brushing, click-farm, information leakage etc; Analyze business and security data, uncover evolving attack motion, identify weaknesses and opportunities in risk defense solutions, explore new space from the discoveries; Define risk control measurements. Quantify, generalize and monitor risk related business and operational metrics. Align risk teams and their stakeholders on risk control numeric goals, promote impact-oriented, data-driven data science practices for risks Bachelor or degrees above in computer science, statistics, math, internet security or other relevant STEM majors (e.g. finance if applying for financial fraud roles); At least 5 years with solid data science skills. Proficiency in statistical analytical tools, such as SQL, R and Python; Familiarity with machine learning or social/content online platform analytics. Bonus given to proficiency in modern machine learning applications
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