TikTok Shop - Anti-Fraud Data Scientist

Responsibilities

About the Team
The TikTok E-commerce Governance and Experience team is responsible for ensuring our marketplace is safe and trustworthy for users, sellers, and creators globally. We value user satisfaction and work on policies, rules, and systems to ensure quality for our community.

As part of the Governance and Experience (GNE) organization, our Risk Control and Security team safeguards TikTok Shop's global marketplace for users, sellers, and creators. We blend data-driven insights, policy design, and technical innovation to combat fraud while preserving a seamless user experience.

Responsibilities
- Define, standardize and monitor key performance metrics, building robust reporting models for effective tracking, evaluation, and continuous improvement.
- Conduct deep-dive RCA for fraud metric anomalies, collaborate with product/algo/engineering teams to optimize detection mechanism, and deliver actionable mitigation actions.
- Design A/B experiments to evaluate model/strategy effectiveness, calculate ROI of anti-fraud initiatives, and scale high-impact solutions across global markets.
- Leverage AI driven approaches to build and iterate scalable tooling to facilitate monitoring and analytics.
- Lead cross-functional projects to address findings and present insights to key stakeholders and manage timeline of key projects.

Qualifications

Minimum Qualification(s)
- Bachelor's degree or above in Mathematics, Statistics, Computer Science, Business Analytic, or related fields (or post-graduation experience in data modeling/analysis).
- Proficient in SQL and Tableau; skilled in Python/R for data analysis, model building, and visualization.
- Strong ability to process complex datasets, identify fraud patterns, and translate insights into business actions.
- Problem-solving skills to address evolving fraud challenges.

Preferred Qualification(s)
- Ecommerce anti-fraud experience (e.g., transaction fraud detection, ATO prevention) and GNE-related experience is a strong plus.
- Global market analysis experience or cross-regional project leadership experience.
- Experience with ML model lifecycle management (training, deployment, monitoring) and A/B experiment design for risk initiatives.
- Experience with AI based analytics tooling is preferred.