Business Data Scientist, YouTube Trust and Safety

• 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.

Minimum qualifications:

• Master's 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).

• Master's 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.