Join YouTube's proactive Trust & Safety team, dedicated to making YouTube a secure platform for users and creators worldwide. This role is crucial in identifying and swiftly removing disruptive content and users in a high-volume, global environment. We are seeking individuals passionate about technology, experienced in analyzing large-scale data and review systems, and adept at thriving in fast-paced, demanding conditions.

As an Engineering Analyst, you will be a key contributor to strategies for reducing online harm. You'll be an independent, critical thinker with strong analytical judgment, capable of coaching others, tackling technological challenges, and establishing scalable standards. Balancing immediate needs with long-term vision, you will make quantitatively driven recommendations.

This position may involve rotational on-call duties and exposure to sensitive video content, aligning with YouTube's Community Guidelines.

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

Refine prompts for Large Language Models (LLMs) to boost accuracy in identifying and classifying abusive content and behavior. Employ advanced statistical methods on complex datasets to assess the impact of abuse on the YouTube ecosystem.

Contribute to the strategy and development of new workflows to combat emerging vectors of abuse. Master intricate technical concepts and systems to deliver impactful results, effectively communicating technical findings and methodologies. Foster and uphold quality by providing regular feedback metrics to the global team, managing technological solutions for quality assurance, and creating scalable training for new workflows.

A Bachelor's degree or equivalent practical experience is required, alongside a minimum of two years in data analysis, encompassing trend identification, summary statistics generation, and deriving insights from quantitative and qualitative data. Experience managing projects, including defining scope, goals, and deliverables, for at least two years is also essential.

Preferred qualifications include a Master's degree in a quantitative field and two years of experience or familiarity with programming languages such as SQL, R, Python, or C++. Experience with machine learning systems for at least two years is beneficial.

Additional desirable experience includes innovating and completing projects with global teams, collecting, managing, and synthesizing large datasets from various sources, statistical modeling, data mining, and data analysis. Excellent written and verbal communication skills are also highly valued.