TikTok Shop – Fraud Investigations and Risk Operations Analyst

Overview

In this role, you act as regional risk owner to build, monitor, and refine processes that detect and prevent fraud across transactions, KYC, onboarding, and seller-creator affiliations. You will bridge regional risk with global frameworks and drive AI-enabled improvements to scale defense measures. You collaborate with cross-functional partners and regulators to align risk strategy with business goals, while delivering actionable insights from data. This is a strategic, hands-on position at the intersection of risk, operations, and AI-powered optimization.

Responsibilities

• Lead investigations and incident management to protect platform integrity and resolve escalations end-to-end.
• Own regional risk strategy by calibrating thresholds and launching new defense approaches to balance security and growth.
• Monitor threats and identify vulnerabilities to proactively mitigate fraud before it scales.
• Collaborate with business, product, data, security/risk, legal, public affairs, and regulators to align risk activity with commercial goals and local inquiries.
• Produce regular high-quality fraud reports on trends, model performance, and business impact to guide regional strategy.
• Define AI opportunities for operational processes and ideally develop AI agents to automate investigations and scale risk detection.

Key requirements

• 4+ years of experience in operations, investigations, or risk strategy related to fraud, abuse, or cybercrime
• Strong strategic decision-making with regional ownership and autonomous risk threshold setting
• Proficiency in SQL and ability to extract insights from complex datasets
• Ability to gather information from diverse online sources and community groups with a balanced, analytical approach
• AI & model literacy: ability to interpret ML model performance metrics and translate into operations

• cross-functional collaboration
• data-driven decision making
• effective communication
• SQL
• Python (preferred)
• understanding of ML performance metrics (precision, recall, data drift)

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