Engineering Analyst, Trust and Safety Monetization

About the job

The mission of our team is to build an ads ecosystem by ensuring safe and healthy growth for publishers, advertisers, and end-users. We defend Google's ad products from fraud and abuse. We evaluate millions of publishers, analyze billions of events, and create scalable methods to stop bad actors and motivated fraudsters from causing harm. We understand the user's point of view and are passionate about using our technical, sales, and customer service acumen to protect them. When we get this right, we help users, advertisers, publishers, and Google harness information and monetize the internet safely and usefully.

Responsibilities

• Perform analysis and investigations using a variety of data sources and take enforcement actions to identify and defend novel fraud and abuse on Google's ad products.
• Partner closely with Engineering teams to improve ad traffic infrastructure, defense systems, and workflows. Proactively identify and help implement efficiency improvements through advanced machine learning techniques, scaled defenses, and automation.
• Drive full lifecycle projects across Product, Engineering, and Trust and Safety teams to prevent abuse by improving policies and closing product vulnerabilities.
• Work collaboratively with team mates around the globe and deliver projects from beginning to end in a timely manner.
• Be able to review or be exposed to sensitive content.

Qualifications

Minimum qualifications:

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

Preferred qualifications:

• Master's degree in a quantitative discipline.
• 2 years of experience with one or more of the following languages: SQL, R, Python, or C++.
• Knowledge of one or more of the following 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.