Account Risk Management Analyst - Trust & Safety

ove 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.