Associate Principal Analyst, Scams and Verification, Trust and Safety

Google Google · Big Tech · Seattle, WA +2

This role focuses on protecting users from offline scams by using data analysis, applied AI, and machine learning to provide recommendations, devise solutions, and optimize human review processes. The analyst will collaborate with engineers to develop algorithms, identify automation opportunities, and build predictive models to enhance detection systems and ensure user safety within Google's products.

What you'd actually do

  1. Collaborate with cross-functional groups such as product manager, engineering, sales, and legal to drive projects that increase users safety and provide better online experience through Google Ads.
  2. Apply advanced statistical methods and technical analysis to uncover sophisticated abuse vectors. Lead in-depth search investigations into complex tactics, techniques, and procedures to perform actor tracking and disrupt organized fraud rings focusing on Google Ads
  3. Identify automation and efficiency opportunities and drive solutions through analysis or cross-functional partnerships.
  4. Analyze massive datasets to identify anomalies and build predictive visual models that influence the evolution of automated detection systems.
  5. Provide periodic on-call coverage and drive the rapid response for high-severity adversarial incidents, conduct thorough root cause analyses to implement long-term structural preventions. Work with sensitive content or situations and may be exposed to graphic, controversial, or upsetting topics or content.

Skills

Required

  • data analytics
  • Trust and Safety
  • policy
  • cybersecurity
  • SQL
  • Python

Nice to have

  • machine learning
  • building dashboards
  • data collection/transformation
  • visualization/dashboards
  • scripting/programming language
  • written and verbal communication
  • presentation skills
  • problem-solving
  • critical thinking
  • attention to detail

What the JD emphasized

  • applied AI
  • machine learning
  • algorithms
  • predictive visual models
  • automation opportunities

Other signals

  • applied AI
  • machine learning
  • algorithms
  • predictive visual models
  • automation opportunities