Scaled Abuse Analyst, Trust and Safety, YouTube

Minimum qualifications:

• Bachelor's degree in Computer Science, Engineering, Statistics, Mathematics, a related field, 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 managing projects and defining project scope, goals, and deliverables.
  
• Experience managing partnerships with Engineering, Product, and Operations teams.
  

Preferred qualifications:

• Experience in data management, metrics analysis, experiment design and automation.
  
• Experience with classification systems, ranking systems or similar.
  
• Experience creating opportunities to innovate, conceptualize and complete while working with global teams.
  
• Experience collecting, managing and synthesizing large data sets and information from disparate sources, statistical modeling, data mining and data analysis.
  
• Experience translating investigative insights into business strategies and actions.
  

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 belong, 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 are passionate about cutting-edge technologies and have experience with analyzing large scale data and scaled systems of review. You thrive in a fast, dynamic, demanding environment where no two days are ever the same. In this role, you will be a key contributor to the strategy for reducing network badness on YouTube. You are an independent, critical thinker with sound metrics judgment who has a passion for coaching others, solving technological challenges and setting scalable standards. You can balance the short term requirements we face while demonstrating the ability to see the big picture and make quantitatively based long-term recommendations accordingly.

This role may require on-call work at weekends on a rotational basis. This role may also 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 listen, 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 cutting-edge 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.

Responsibilities

• Develop high quality training materials, create consensus and maintain communication for YouTube Scaled Abuse with business stakeholders.
  
• Apply advanced statistical methods to complex data sets to understand the impact of abuse to the YouTube ecosystem. Contribute strategy and development of new workflows against additional known vectors of abuse.
  
• Maintain, foster and promote quality by providing regular feedback metrics to the Global team. Manage technological solutions for streamlining quality assurance and produce scalable training solutions to emergent workflows.
  
• Perform fraud and spam investigations using various data sources, identify product vulnerabilities and drive anti-abuse experiments to prevent abuse. Work with engineers and interact cross-functionally with stakeholders to improve workflows.
  
• Apply and develop machine learning models (e.g., using LLMs, BERT). Develop and improve machine learning-based detection systems for different adversarial and large scale coordinated abuse vectors.