Join our Trust & Safety team, a critical part of Google, dedicated to safeguarding our products and users. As an Engineering Analyst, you will tackle complex challenges, leveraging technical expertise and strategic thinking to combat abuse and fraud across Google's vast ecosystem. This role offers the opportunity to make a significant impact by enhancing user trust and ensuring the integrity of our services.

This team is composed of experts in abuse prevention and user trust, working collaboratively to create a safer online environment. We partner with various Google teams to implement robust solutions against threats like malware, spam, and account hijacking, protecting users, advertisers, and publishers globally in numerous languages.

Promote user trust and safety by actively managing and mitigating payment fraud and abuse impacting Google's products and services. Conduct thorough investigations into fraud and abuse incidents, identifying key patterns and trends to develop comprehensive risk management strategies.

Perform end-to-end assessments of product and feature vulnerabilities. Design and implement effective fraud and abuse mitigation strategies. Collaborate closely with engineering and product teams to enhance tools, develop predictive signals, and improve system accuracy and efficiency. Engage with global cross-functional teams, including engineers and product managers, to successfully launch risk mitigation solutions.

Participate in an on-call rotation to address high-priority escalations outside of standard working hours, including weekends and holidays. This ensures continuous protection and rapid response to critical incidents.

A Bachelor's degree or equivalent practical experience is required. Candidates must possess at least two years of experience in data analysis, encompassing trend identification, summary statistics generation, and deriving insights from quantitative and qualitative data. Additionally, two years of experience in project management, including defining scope, goals, and deliverables, is essential.

Preferred qualifications include four years of experience in the Payments industry, specifically focusing on risk or fraud management. Experience with querying and analyzing large datasets is highly valued. Strong project and program management skills are a must. Familiarity with statistical analysis and machine learning libraries (e.g., R, Scikit-learn, TensorFlow), programming languages (e.g., Python, C/C++), Large Language Models (LLMs), or Generative AI is advantageous. Demonstrated ability to identify workflow inefficiencies, optimize processes, and scale operations is also beneficial.