Observability Data Scientist: Trust & Safety Insights

TikTok Inc.; San Jose (CA); On-site; USD 179,000 - 306,000; Full time

Benefits offered by this job

Medical, dental, vision insurance

401(k) with company match

Paid parental leave

Disability coverage

Wellbeing benefits

Paid holidays and personal time

Job summary

TikTok is seeking a Data Scientist for the Transparency & Observability team within Trust & Safety in San Jose. You will develop scalable metrics, monitoring systems, and analytical solutions to improve visibility into platform safety and enforcement performance.

You will design and implement dashboards, anomaly detection, and data quality improvements while collaborating with Product, Engineering, Policy, and Data teams to drive strategic decisions and strengthen observability for transparency

Qualifications

• 5+ years of experience in Data Science, Data Analytics, Data Engineering, or related quantitative field.
• Proficiency in SQL, Python or R.
• Experience building data quality monitoring, reporting, or observability solutions.

Responsibilities

• Build Trust & Safety metrics and monitoring across products and content types.
• Design metrics to measure enforcement and provide actionable insights.
• Build dashboards and monitoring solutions for platform safety.
• Develop automated anomaly detection and alerting for data or operational issues.
• Partner with engineering to improve data reliability and governance.
• Work cross-functionally with Product, Engineering, Policy, Operations, and Data teams to scale transparency reporting and observability.

Skills

SQL

Python

R

Data analytics

Communication

Education

Bachelor's degree in a quantitative field

Tools

Hive

Spark

Job description

TikTok is seeking a Data Scientist for the Transparency & Observability team within Trust & Safety in San Jose. You will develop scalable metrics, monitoring systems, and analytical solutions to improve visibility into platform safety and enforcement performance.

You will design and implement dashboards, anomaly detection, and data quality improvements while collaborating with Product, Engineering, Policy, and Data teams to drive strategic decisions and strengthen observability for transparency