Lead Data Scientist, Transparency & Observability

TikTok; San Jose (CA); On-site; USD 219,000 - 354,000; Full time

Job summary

TikTok is seeking a Lead Data Scientist for Transparency & Observability in the Trust & Safety group, based in San Jose. You will architect observability systems, establish data governance, and drive AI readiness across product and policy teams.

You will collaborate with Product, Engineering, Policy, and Legal to translate complex problems into scalable data strategies and ensure explainable, auditable outcomes across enforcement workflows.

Qualifications

• 5+ years in Data Science, Data Engineering, Data Architecture, Analytics, or related field with cross-team leadership.
• Experience designing scalable data ecosystems, data modeling, and production pipelines on large-scale platforms.
• Strong SQL and Python skills; experience with data quality, observability, and metrics frameworks.

Responsibilities

• Architect Transparency & Observability Systems for Trust & Safety, including metrics, data pipelines, monitoring, and reporting.
• Design end-to-end observability for enforcement lifecycle with traceability and explainability of decisions.
• Lead data governance, data quality, lineage, and regulatory reporting readiness.
• Oversee ETL pipelines and workflow orchestration to produce reliable data products.
• Partner with Product, Engineering, Policy, Operations, Legal, and Comms to apply AI and data to improve efficiency and safety outcomes.
• Own long-term technical roadmap for Trust & Safety observability and AI readiness, and mentor data scientists and engineers.

Skills

Data strategy

SQL

Python

Education

Bachelor's degree or higher in a quantitative field

Tools

Hive

Spark

Job description

TikTok is seeking a Lead Data Scientist for Transparency & Observability in the Trust & Safety group, based in San Jose. You will architect observability systems, establish data governance, and drive AI readiness across product and policy teams.

You will collaborate with Product, Engineering, Policy, and Legal to translate complex problems into scalable data strategies and ensure explainable, auditable outcomes across enforcement workflows.