Senior Scaled Abuse Analyst, YouTube Traffic Abuse

Minimum qualifications:

• Bachelor's degree or equivalent practical experience.
• 5 years of experience in data analysis, including identifying trends, generating summary statistics, and drawing insights from quantitative and qualitative data.
• 5 years of experience in managing projects and defining project scope, goals, and deliverables.
• Experience with data analysis, statistical analysis, and data science.

Preferred qualifications:

• Master's degree in a quantitative field (e.g., Statistics, Econometrics, Computer Science, Mathematics, Engineering).
• 5 years of experience in a data-intensive role such as threat intelligence, data science , trust and safety, or fraud analysis, with specific exposure to anti-abuse, network analysis, or bot/scraper detection.
• 5 years of experience or familiarity with one or more of the following languages: SQL, R, Python or C++.
• Experience with or understanding of machine learning systems, large language models (LLMs), and agentic AI behaviors.
• Excellent written and verbal communication skills.

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.

As a Scaled Abuse Analyst, you will be at the forefront of protecting YouTube from scraping, automated abuse, and manipulation. You will use your analytical skills to understand the evolving bot and scraper landscape, identify new inorganic traffic patterns, and measure the effectiveness of our protections. You will grow in a fast, dynamic, demanding environment where no two days are ever the same. You will be a key contributor to the strategy for reducing network badness on YouTube, owning complex, multi-quarter anti-abuse problems from scoping through delivery. You will design and deploy detection strategies, countermeasures, and workflows from the ground up to ensure a high-quality, reliable platform experience against adversarial actors.

You will be an independent, critical thinker with sound metrics judgment who has a passion for solving technological challenges, tracking threat actors in the scraper ecosystem, and setting scalable standards for the team. You will leverage extensive data analysis and ML/AI, identify abuse trends and evaluate the impact of our anti-bot protections on both adversaries and real users. You may be exposed to graphic, controversial, and sometimes offensive video content during team escalations in line with YouTube's Community Guidelines.At YouTube, we believe that everyone deserves to have a voice, and that the world is a better place when we 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 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.Individual pay is determined by factors including job-related skills, experience, and relevant education or training.

US: $159000 - $230000 (USD) + 15% bonus target + equity + benefits

Learn more about benefits at Google.

Responsibilities:

• Investigate and analyze complex patterns of abuse on YouTube, utilizing data-driven insights to develop countermeasures against scraping, and automated abuse.
• Analyze log datasets to identify trends, patterns, and anomalies that indicate inorganic traffic or malicious automated behavior within the YouTube ecosystem.
• Perform investigations using various data sources, identify product vulnerabilities, and drive anti-abuse experiments to measure the effectiveness of our protections against bots and scrapers.
• Collaborate with engineering teams to design, test, and launch new anti-bot rules, ML models, and system enhancements, while carefully evaluating the impact on real users.
• Leverage AI / ML to identify, track, address traffic abuse, and to evaluate the effectiveness of our abuse countermeasures.