Engineering Analyst, Trust and Safety

About the job

At Google we work hard to earn our users trust every day. Gaining and retaining this trust is critically important to Google’s success. We defend Google's integrity by fighting spam, fraud and abuse, and develop and communicate product policies. The Trust and Safety team reduces risk and protects the experience of our users and business partners across Google's expanding base of products. We work with a variety of teams from Engineering to Legal and Public Policy to set policies and combat fraud and abuse.

Responsibilities

• Partner with Search teams on project scoping, risk assessments and prioritization and lead projects and cross-functional initiatives within Google, interacting with executive stakeholders from Engineering, Legal, Product teams and more while becoming an expert in Search infrastructure, ranking signals and Search features to Build Large Language Model (LLM) based models that can evaluate content safety according to our product policies.
• Design and implement product metrics to benchmark user trust risks and track improvements over time and creating datasets for engineers to evaluate and improve sensitive content classifiers while delivering leadership and impact as for the broader Trust and Safety Search, Assistant and Geo team.
• Review or be exposed to sensitive or violative content as part of the core role.
• Perform on-call responsibilities on a rotating basis, including weekend coverage/holidays.
  

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:

• Experience working with abuse, spam, fraud, or malware.
• Experience working on product policy analysis and identifying policy risks.
• Experience using statistical analysis and hypothesis testing.
• Experience analyzing ML models performance or working on LLMs.
• Excellent problem-solving and critical thinking skills with attention to detail in an ever-changing environment.