The Account Risk Management team safeguards TikTok's ecosystem by proactively identifying, mitigating, and preventing account-level abuse.
The team combines behavioural, 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 rigour, 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 behavioural, device, and graph signals to detect emerging attack patterns and continuously improve system precision and recall.
- Conduct research and development on behavioural 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.
Minimum Qualifications: - Bachelor's degree in a relevant field (., Data Science, Computer Science, Statistics, or related discipline) or equivalent experience.
- Atleast 5 years of experience in risk analytics, trust able to distill complex analytical findings for diverse audiences.
Preferred Qualifications: - Practical experience applying AI/ML techniques to solve diverse business challenges - Hands on experience with big data technologies such as Apache Spark and Hive for large scale data processing and analysis.
- Background in trust and safety-focused roles, with a track record of mitigating risks and ensuring platform integrity.
- Strong problem-solving and analytical skills, demonstrated through the ability to deconstruct complex issues, identify root causes, and deliver actionable insights.