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- 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.
- 6 years of experience in a large global business managing business outcomes using data analysis, statistical analysis, data pipelines (e.g., SQL), and dashboard solutions (e.g., Tableau, Qlik).
- 3 years of experience utilizing data science techniques to solve business issues, including using statistical modeling techniques and programming languages (e.g., Python, R) for data analysis.
- Experience in Trust and Safety, content moderation, or risk analysis.
- Experience in leading technical projects, influencing cross-functional teams and navigating ambiguous environments.
- Excellent communication, people management, and stakeholder management skills, with the ability to translate data into clear, insights for non-technical audiences.
In this role, you will be responsible for designing and developing metrics and measurements needed to support Trust and Safety across various Google products, including Search, Ads, Shopping, Publishers, and YouTube. You will ensure stakeholders have the product, support, and operations data they need to make crucial business decisions. You will have the opportunity to design data solutions and solve testing problems using Google’s production data infrastructure to drive business optimization and meet regulatory reporting requirements.
Responsibilities
- Develop understanding of product, Trust and Safety manual and automated processes, tools and customer expectations. Design and develop scalable measurements to support reporting needs.
- 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.
- Design, implement and own technical implementation of production-level data pipelines, documentation, check-in process, etc.
- Build and scale AI-based content rating systems in partnership with Engineering teams, accurately measure the efficiency gains from the same.
- Collaborate with and influence business and engineering stakeholders to ensure our data is telling the right story and is helping stakeholders make data backed decisions. Present findings and business recommendations to multiple levels of stakeholders and leadership teams.
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