Engineering Analyst, Trust and Safety, Ads

About The Job

Trust & Safety team members identify and address significant challenges to the safety and integrity of Google's products. They leverage technical expertise, problem-solving abilities, user insights, and proactive communication to protect users and partners from abuse across Google products such as Search, Maps, Gmail, and Google Ads. This role involves being a big-picture thinker and strategic team player passionate about doing what’s right. You will work globally and cross-functionally with Google engineers and product managers to identify and combat abuse and fraud cases with urgency, contributing to Google's reputation for trust and user safety.

As an Engineering Analyst, you will focus on combating adversarial actors within Google Ads. You will apply advancements in AI/ML and develop scalable solutions to protect internet users from scams and real-world harm. You will collaborate with Product Managers, Software Engineers (ML/Anti-Abuse and Security Engineers), Policy, Sales, and Review teams to devise strategies to outsmart adversaries. Specific tasks include developing data-driven insights to identify new trends in adversarial fraud, automating enforcement using agentic AI, feature engineering/signal development for ML model improvements, reverse engineering payloads, and developing intelligence on threat actors and their TTPs (Tactics, Techniques, and Procedures).

Google is committed to earning user trust daily. The Trust and Safety team consists of abuse-fighting and user trust experts dedicated to making the internet a safer place. They partner with teams across Google to deliver bold solutions in areas like malware, spam, and account hijacking. This team comprises Analysts, Policy Specialists, Engineers, and Program Managers who work to reduce risk and fight abuse across all Google products, protecting users, advertisers, and publishers globally in over 40 languages.

Key Responsibilities

• Lead in-depth investigations into complex TTPs to track actors and disrupt organized fraud rings targeting Google Ads.
• Apply advanced statistical methods and technical analysis to uncover sophisticated abuse vectors, including malware distribution, cloaking, and policy circumvention.
• Identify anomalies and build predictive visual models to influence the evolution of automated detection systems.
• Drive rapid response for high-severity adversarial incidents, conducting thorough root cause analyses to implement long-term preventions.
• Design and execute anti-abuse experiments to identify product shortcomings and stress-test Google’s protection measures against evolving scam patterns.
• Perform on-call responsibilities on a rotating basis, including weekend coverage/holidays.
• Work with sensitive content or situations, which may include exposure to graphic, controversial, or upsetting topics or content.

Requirements & Tech Stack

• Experience in data analysis, including identifying trends, generating summary statistics, and drawing insights from quantitative and qualitative data.
• Experience managing projects and defining project scope, goals, and deliverables.
• Familiarity with one or more of the following languages: SQL, R, Python, or C++.
• Familiarity with machine learning systems.
• Experience in fighting adversarial actors in Google Ads.
• Ability to apply advancements in AI/ML.
• Develop scaled solutions to protect internet users from scams and real-world harm.
• Work with Product Managers, Software Engineers (ML/Anti-Abuse and Security Engineers), Policy, Sales, and Review teams.
• Develop data-driven insights to identify new trends in adversarial fraud.
• Automate enforcement using agentic AI.
• Feature engineering/signal development for ML model improvements.
• Reverse engineering payloads.
• Develop intelligence on threat actors, TTPs, etc.
• Apply advanced statistical methods and technical analysis.
• Identify anomalies and build predictive visual models.
• Conduct thorough root cause analyses.
• Design and execute anti-abuse experiments.

Qualifications

• Bachelor's degree or equivalent practical experience.
• 2 years of experience in data analysis.
• 2 years of experience managing projects.
• 2 years of experience or familiarity with one or more of the following languages: SQL, R, Python, or C++.
• 2 years of experience or familiarity with machine learning systems.
• Master's degree in a quantitative discipline (Preferred).
• Excellent problem-solving and critical thinking skills with attention to detail in an ever-changing environment.
• Excellent written and verbal communication skills.