Account Risk Management Analyst - Trust & Safety

Responsibilities

About the Team
The Account Risk Management team safeguards TikTok's ecosystem by proactively identifying, mitigating, and preventing account-level abuse.
The team combines behavioral, device, and network intelligence with automation and applied machine learning to strengthen the integrity of the platform and reduce risk at scale. Our solutions are designed to detect and disrupt sophisticated threats while ensuring a secure and trustworthy experience for users.

We're looking for a technically strong and analytically curious Analyst to join the Account Risk Management team.
You will develop automated workflows and analytical frameworks to detect malicious accounts and coordinated networks at scale.
This role combines investigative rigor, signal experimentation, and applied ML - ideal for someone who enjoys building scalable detection systems that improve both platform safety and operational efficiency.

- Automate detection and investigation workflows to identify coordinated networks and high-risk account clusters engaged in impersonation, spam, or policy evasion.
- Design and evaluate analytical frameworks that leverage behavioral, device, and graph signals to detect emerging attack patterns and continuously improve system precision and recall.
- Conduct research and development on behavioral signals - identify, design, and validate new features or attributes that improve detection coverage and enforcement precision.
- Fine-tune and operationalize machine learning and anomaly-detection models to assess account-level risk and surface suspicious patterns in near real time.
- Collaborate cross-functionally with Investigations, Applied Science, and Product teams to improve detection signal quality, model inputs, and decision efficiency.
- Communicate insights through concise reports, dashboards, and presentations that drive executive and operational decisions.
- Contribute to continuous improvement of detection and enforcement pipelines through rule refinement, feedback loops, and model retraining cycles.

Qualifications

Minimum Qualifications
- Bachelor's degree in Data Science, Computer Science, Statistics, or related discipline.
- 5 years of experience using SQL and Python for data exploration, analysis, and pipeline automation.
- Experience applying ML and anomaly-detection techniques to large, adversarial datasets.
- Experience with graph analytics or entity-resolution techniques for detecting coordinated or linked behaviors.
- Strong communication skills; able to distill complex analytical findings for diverse audiences.

Preferred Qualifications
- Candidate with risk analytics, trust & safety, fraud, or intelligence analysis background.
- Candidate with Data Streaming background.