Ecommerce Anti-fraud Analyst

1 week ago

Singapore TIKTOK PTE. LTD. Full time

Ecommerce's Governance and Experience(GNE) is a global team responsible for ensuring our marketplace is safe and trustworthy for not only users but also sellers and creators. We value user satisfaction and work on policies, rules, and systems to ensure quality. As the Anti-fraud analyst, you will be responsible for building, improving, and monitoring business processes that detect and prevent fraudulent activities in various scenarios including transactions, KYC, and seller-creator affiliation.

Roles & Responsibilties:

- Collaborate with product managers, data scientists, and the engineering team to evaluate the efficiency of existing tools, processes, and policies related to fraud management;
- Understand the trends and intelligence of fraudulent activities in each local market and build intelligence systems to mitigate fraud;
- Define key performance metrics and build detailed reporting models for effective tracking, reporting, and feedback on measures implemented;
- Drive process implementation and improvement that streamlines determination of resellers, incentive abuse, and/or fake order evaluation;
- Communicate findings and complex results to key stakeholders and manage timeline of mitigation projects.

Qualifications:

- Bachelor's degree or above with a background in Computer Science, Business Analytic, Math, Statistics, or related field ;
- Strong data analysis and correlation skills with ability to work through complex and unfamiliar data sets and able to derive the characteristics of different scenarios;
- Comfortable in a matrix organization and to drive cross-functional and cross-regional collaborations;
- Strong attention to detail and ability to notice discrepancies in data;
- Experience with SQL and analytical tools (SAS, R, Python) is a must;
- Experience with AI and machine learning is a plus;
- Familiarity with risk and fraud in Ecommerce fields is a strong plus;
- Past experience working in SEA or EU market is highly preferred;

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