Anti-Fraud Data Science Project Intern (TikTok Shop)

About the Team

TikTok Shop's Governance and Experience (GNE) is a global team responsible for ensuring our marketplace is safe and trustworthy for not only our users, but also sellers and creators. We value user satisfaction and work on policies, rules and systems to ensure quality. Our mission is to provide world-class service and experience for customers, sellers, and creators.

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. The Ecommerce Anti-Fraud Data Scientist role sits at the intersection of defining success metrics system, risk analytics, and cross-functional collaboration to monitor and mitigate fraudulent activities.

As a Project Intern, you will contribute to impactful short-term projects and gain hands-on experience in a fast-paced, professional environment. This internship offers the opportunity to develop practical skills, apply your knowledge to real-world challenges, and explore your career interests.

Applications are reviewed on a rolling basis, so we encourage you to apply early.

Responsibilities

• 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.
• Leverage AI tools to build scalable data assets to facilitate monitoring and analytics.
• Design and implement A/B experiments to evaluate model/strategy effectiveness, calculate ROI of anti-fraud initiatives, and scale high-impact solutions across global markets.

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.
• Structured and logical thinking with problem solving skills.
• Ability to thrive in a fast-paced, ambiguous environment.

Preferred Qualification(s)

• Strong ability to process complex datasets, identify fraud patterns, and translate insights into business actions.

If you have any questions, please reach out to us at [email protected]