Business Data Scientist YouTube Trust And Safety

Minimum qualifications:

• Masters degree in a quantitative discipline such as Statistics, Engineering, Sciences, or equivalent practical experience.
• 4 years of experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis.
• 4 years of experience using SQL to extract and manage quantitative data over distributed databases.

Preferred qualifications:

• 7 years of experience in data analysis, data science, or a related quantitative role in Trust and Safety, abuse detection, content moderation, or large-scale operations.
• 6 years of experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis.
• Expertise in statistical sampling theory, clustered experimental design (e.g., Intra-Cluster Correlation/Design Effect), variance estimation, and confidence interval modeling.
• Proficiency in SQL for complex data extraction, transformation, and query optimization.
• Advanced proficiency in a programming language commonly used in data analysis, such as Python or R (e.g., Pandas, NumPy, Statsmodels, Scikit-Learn).

About the job:

Fast-paced, dynamic, and proactive, YouTube’s Trust & Safety team is dedicated to making YouTube a safe place for users, viewers, and content creators around the world to create, and express themselves. Whether understanding and solving their online content concerns, navigating within global legal frameworks, or writing and enforcing worldwide policy, the Trust & Safety team is on the frontlines of enhancing the YouTube experience, building internet safety, and protecting free speech in our ever-evolving digital world.

The Trust and Safety Analytics team operates in a high-stakes, dynamic environment, providing comprehensive analytics for content policy enforcements that rely on complex tooling, scaled machine learning, and hybrid human-Artificial Intelligence (AI) review systems. We manage a rigorous cadence of executive reporting, where frequent and significant policy and automation changes directly impact safety metrics, enforcement precision, root cause analyses, and core user experience metrics (such as creator appeals and reinstatements). As the platform accelerates automated enforcement and AI-assisted review, this role is critical in navigating increasing complexity, assessing quality-vs-cost tradeoffs across extended workforces, designing advanced operational experiments, and driving strategic decision-making across detection pipelines and global operations.

At YouTube, we believe that everyone deserves to have a voice, and that the world is a better place when we listen, share, and build community through our stories. We work together to give everyone the power to share their story, explore what they love, and connect with one another in the process. Working at the intersection of cutting-edge technology and boundless creativity, we move at the speed of culture with a shared goal to show people the world. We explore new ideas, solve real problems, and have fun — and we do it all together.

Responsibilities:

• Own, maintain, and advance statistical methodologies and sampling frameworks for system-level enforcement quality and machine learning precision monitoring across 200+ production classifiers, ensuring high-confidence measurement.
• Lead the quantitative analytics and data science strategy for automated content moderation, designing post-launch impact assessment frameworks and launch readiness criteria for automated policy defense systems.
  
• Design and execute large-scale, statistical experiments to evaluate human decision-making under varying operational conditions, modeling rater behavior to optimize review queue architecture and workforce routing.
• Formulate and execute rigorous multivariate Root Cause Analysis (RCA) and causal inference frameworks to resolve complex operational anomalies, isolating key drivers of efficiency and steering executive decision-making with empirical data.
  
• Partner with Software Engineering, Data Science, Product Management, and Global Operations teams to deploy scalable metrics infrastructure, validate technical solutions, and influence product and policy roadmaps.