Partner with Trust and Safety leadership to shape critical policy decisions, conduct root cause and data-driven analyses and identify emerging trends that impact online safety. Collaborate across teams to implement solutions, enhance user protection, safeguard free expression and strengthen YouTube's global community.
Requirements
- 2 years of experience in statistical problem solving and analyzing data sets using SQL or comparable coding language (e.g., Python, R, Java, C++).
- 1 year of experience working in data analytics, consulting, data science , engineering, or a technical operations role.
- 1 year of experience with designing experiments, developing statistical models, measurement, and identifying business opportunities.
- Experience in data analysis, with the ability to solve problems in changing and ambiguous business environments through data intuition and business acumen.
- Knowledge of Google tools such as Plex, BQ, etc
Responsibilities
- Develop and measure performance on operational metrics such as productivity, service level agreements, quality, cost, efficiency and timeliness to generate insights that improve operational efficiency.
- Analyze the user impact of policies, master key metrics and conduct ongoing, ad-hoc and detailed assessments that provide actionable insights to enhance user experience, support Trust and Safety objectives and strengthen operational effectiveness and incident response.
- Collaborate with Operations, Engineering, Legal, PR and Product Management teams to craft data-driven narratives for policymakers, executives and governments.
- Co-ordinate with data science and strategy teams to prioritize and execute sophisticated quantitative analyses and advanced modeling that deliver actionable insights.
Other
- Ability to effectively communicate analysis and recommendations across a wide range of non-technical audiences.
- Bachelor's degree in Engineering, Math, Quantitative Science, or related technical field, or equivalent practical experience.
- Master's degree in a quantitative field (e.g., Statistics, Computer Science, Engineering, Mathematics, Data Science, etc.).
