Join a dedicated team focused on building a secure and thriving ads ecosystem. We are committed to ensuring safe and sustainable growth for publishers, advertisers, and users by defending Google's ad products against fraud and abuse. Our work involves evaluating millions of publishers, analyzing billions of events, and developing robust methods to combat malicious actors and sophisticated fraudsters. We prioritize the user experience and leverage our technical, sales, and customer service expertise to provide effective protection, ultimately enabling users, advertisers, publishers, and Google to harness information and monetize the internet securely and beneficially.

Analyze and investigate diverse data sources to identify and combat novel fraud and abuse on Google's ad platforms, implementing necessary enforcement actions. Collaborate closely with Engineering teams to enhance ad traffic infrastructure, strengthen defense systems, and optimize workflows. Drive proactive improvements through advanced machine learning techniques, scalable defenses, and automation. Lead comprehensive projects from inception to completion across Product, Engineering, and Trust and Safety departments, focusing on preventing abuse by refining policies and addressing product vulnerabilities. Work effectively with global team members to deliver projects punctually. Be prepared to review or encounter sensitive content as part of the role.

A Bachelor's degree or equivalent practical experience is required. Possess 2 years of experience in data analysis, demonstrating the ability to identify trends, generate summary statistics, and derive insights from both quantitative and qualitative data. Have 2 years of experience in project management, including defining project scope, objectives, and deliverables.

Preferred qualifications include a Master's degree in a quantitative discipline. Demonstrate 2 years of experience with at least one of the following programming languages: SQL, R, Python, or C++. Show knowledge in one or more of these areas: statistical analysis and Machine Learning libraries (e.g., R, Scikit-learn), programming languages (e.g., Python, C/C++), Large Language Models (LLMs), or Generative AI.