Join our Trust and Safety team, dedicated to safeguarding Google Ads products from sophisticated threats. This role involves tackling major integrity challenges with a blend of technical expertise, strategic problem-solving, and proactive communication. You will be a key player in a global, cross-functional effort to protect users and partners from abuse, working at the speed of Google to ensure user trust and safety.

As an Engineering Analyst Manager, you will spearhead a team focused on combating adversarial actors within Google Ads. Your leadership in data science and fraud detection will drive AI transformation, creating scalable solutions to counter threats like malware and cloaking. You'll collaborate with Ads Safety, AP Engineering, and gTech to develop comprehensive defense strategies that prioritize user safety while ensuring a smooth advertiser experience.

Lead and inspire a high-performing team of Engineering Analysts, driving AI innovation, operational excellence, and strategic execution against fraudulent activities.

Collaborate with Product Management, Engineering, and Legal teams to devise robust defense-in-depth strategies that enhance user safety and protect the Google Ads ecosystem from emerging threats.

Spearhead the development of AI-powered enforcement capabilities to proactively combat abuse at scale. Establish cutting-edge evaluation frameworks for the AI era, pioneer rigorous measurement methodologies, and manage programs for training next-generation AI agents and ML models for fraud detection.

Conduct in-depth investigations into complex Tactics, Techniques, and Procedures (TTPs) to track adversaries and dismantle organized fraud rings targeting Google Ads.

Work closely with sales leadership to design and implement efficient recovery processes for advertisers affected by policy enforcement, ensuring a balance between mitigating user harm and optimizing the advertiser experience.

A Bachelor's degree or equivalent practical experience is essential. You should possess at least 7 years of experience in developing technical strategies and implementing data-driven solutions, including building and managing multi-tiered policy enforcement systems. Additionally, 3 years of people management experience, focusing on mentoring analytical and technical teams in applied AI, is required.

Demonstrated experience in translating complex data analyses and emerging abuse trends into actionable business strategies for both technical and non-technical stakeholders, including senior leadership, is crucial.

Preferred qualifications include 10 years of experience in large-scale technical strategy and data-driven solution execution, encompassing the management of intricate, multi-tiered policy enforcement systems. A strong understanding of machine learning lifecycles, human-in-the-loop workflows, precision/recall metrics, and model drift is highly valued. You should also have excellent communication, problem-solving, and critical thinking skills, with a keen eye for detail in dynamic environments.