Join YouTube's dynamic Trust & Safety team, dedicated to making the platform a secure space for users and creators worldwide. As a Scaled Abuse Analyst, you'll be instrumental in developing strategies to combat network abuse. This role requires a critical thinker adept at analyzing large-scale data, solving technological challenges, and setting high standards within a fast-paced environment. You'll balance immediate needs with long-term, data-driven recommendations.

This position involves on-call weekend rotations and exposure to sensitive content as part of escalation procedures, in line with YouTube's Community Guidelines.

Investigate fraud and spam using diverse data sources to pinpoint product vulnerabilities and implement anti-abuse experiments. Collaborate with engineers and cross-functional teams to enhance workflows through process improvements, automation, and the development of anti-abuse systems.

Refine prompts for Large Language Models (LLMs) to accurately detect and classify abusive content, encompassing prompt engineering, data labeling, and performance analysis. Apply advanced statistical methods to large datasets to assess the impact of abuse on the YouTube ecosystem and contribute to new workflow strategies.

Acquire proficiency in complex technical concepts and systems to deliver impactful results. Maintain and promote quality by providing regular feedback metrics to the global team and manage technological solutions for quality assurance streamlining.

A Bachelor's degree or equivalent practical experience is essential. Candidates must possess at least 2 years of experience in data analysis, including trend identification, generating statistical summaries, and deriving insights from both quantitative and qualitative data. A minimum of 2 years of experience in data analytics, Trust and Safety, policy, risk/fraud analytics, or a related field is required.

Proficiency in SQL, R, or Python is mandatory, with 2 years of experience expected. Preferred qualifications include expertise in metrics analysis, experiment design, automation, and translating investigative insights into actionable business strategies. Experience managing and synthesizing large datasets from various sources, coupled with statistical modeling and data mining, is highly valued.