Join our team dedicated to fostering a secure and thriving advertising ecosystem. We safeguard Google's ad products by combating fraud and abuse, analyzing vast datasets, and developing robust solutions against malicious actors. Our focus is on user protection and enabling a safe, valuable online experience for all stakeholders.

We are looking for an Engineering Analyst to contribute to our mission of ensuring the integrity of Google's ad products. This role involves deep analysis, strategic partnerships, and driving impactful projects to protect users and advertisers.

This position offers a unique opportunity to work at the forefront of trust and safety in the digital advertising space, utilizing cutting-edge techniques to maintain a healthy online environment.

Perform in-depth analysis and investigations across diverse data sources to detect and neutralize emerging fraud and abuse on Google's ad platforms. Collaborate closely with Engineering teams to enhance ad traffic infrastructure, fortify defense mechanisms, and streamline operational workflows. Drive the complete project lifecycle, working across Product, Engineering, and Trust and Safety departments to bolster policies and address product vulnerabilities.

Lead proactive identification and implementation of efficiency improvements through advanced machine learning, scalable defenses, and automation. Foster a collaborative environment by working effectively with global team members, ensuring timely project delivery from inception to completion.

Be prepared to review and handle sensitive content as part of ongoing efforts to maintain platform integrity.

A Bachelor's degree or equivalent practical experience is required. Candidates should possess a minimum of two years of experience in data analysis, encompassing trend identification, generation of summary statistics, and deriving insights from quantitative and qualitative data. An additional two years of experience in project management, including defining project scope, goals, and deliverables, is also necessary.

Preferred qualifications include a Master's degree in a quantitative discipline. Experience with at least one programming language such as SQL, R, Python, or C++ is highly desirable. Familiarity with statistical analysis, Machine Learning libraries (e.g., R, Scikit-learn), Large Language Models (LLMs), or Generative AI would be a significant asset.