Minimum qualifications:

• Master's degree in a quantitative discipline such as Statistics, Engineering, Sciences, or equivalent practical experience.
  
• 4 years of experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis.
  
• Experience creating dashboards, data visualizations, or business reports.

Preferred qualifications:

• 6 years of experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis.
  
• 6 years of experience driving business outcomes using data analysis, statistical analysis, data pipelines, and dashboard solutions.
• Experience in Trust and Safety, content moderation, or risk analysis.
• Experience leading complex technical projects, influencing cross-functional teams.
• Experience with agentic statistical modeling, root cause analysis, and large language model (LLM) multi-agent system engineering.
• Excellent communication and stakeholder management skills to translate complex data into clear, actionable insights.

About the job

Google's leadership team hand-picks thorny business challenges, and members of BizOps work in small teams to find solutions. As part of this team you fully immerse yourself in data collection, draw insight from analysis, and then zoom out to develop compelling, synthesized recommendations. Taking strategy one step further, you also persuasively communicate your recommendations to senior-level executives, roll-up your sleeves to help drive implementation and check back-in to see the impact of your recommendations.

Responsibilities

• Develop an understanding of products, Trust and Safety (T&S) manual and automated processes, tools, and customer expectations to design scalable measurements that support reporting needs.
• Build and scale AI-based systems in partnership with Engineering teams, accurately measure the efficiency gains.
  
• Lead the statistical design, definition, and implementation of metrics such as uncaught badness rate, creating standardized, robust, and defensible measurements applicable across products and violation areas.
  
• Collaborate with stakeholders to understand the domain, business goals, and data infrastructure context.