Apply advanced statistical methods using SQL and Python to analyze large, complex datasets to identify abuse trends, develop signals, and provide data-driven recommendations for the YouTube ecosystem.
Collaborate with Engineering, Product, Policy and Vendor teams to create and enhance anti-abuse tools, improve system accuracy; leverage data pipelines to drive automation opportunities.
Investigate vulnerabilities through deep-dive analysis, contribute to developing new workflows against emerging abuse vectors, and design and maintain high-impact, leadership-level dashboards that translate complex anti-abuse metrics into strategic narratives for executive stakeholders.
Drive complex projects through the full life-cycle, partnering with stakeholders in engineering, policy and vendor operations to drive solutions.
Manage project schedules, respond to internal/external escalations within Service Level Agreement (SLAs), and participate in an on-call rotation.
Minimum qualifications:
Bachelor's degree or equivalent practical experience.
2 years of experience in data analysis, including identifying trends, generating summary statistics, and drawing insights from quantitative and qualitative data.
2 years of experience managing projects and defining project scope, goals, and deliverables.
Preferred qualifications:
Master's degree in a quantitative discipline.
2 years of experience or familiarity with one or more of the following languages: SQL, R, Python, or C++.
Experience in data analysis, including identifying trends, generating summary statistics, and drawing actionable insights from both quantitative and qualitative data.
Experience in identifying and mitigating fraud and abuse dynamics within online platforms.
Proficiency in SQL (e.g., BigQuery, MySQL) and Python for data manipulation and analysis along with experience with prompt optimization to tune Large Language Model (LLM)-based models, specifically optimizing for statistical metrics such as precision and recall.
