info_outline XApplicants in San Francisco: Qualified applications with arrest or conviction records will be considered for employment in accordance with the San Francisco Fair Chance Ordinance for Employers and the California Fair Chance Act.Note: By applying to this position you will have an opportunity to share your preferred working location from the following: Mountain View, CA, USA; New York, NY, USA; San Francisco, CA, USA. Minimum qualifications: Bachelor's degree in Statistics, Mathematics, Data Science, Engineering, Physics, Economics, or a related quantitative field. 10 years of experience using analytics to solve product or business problems, performing statistical analysis, and coding (e.g., Python, R, SQL) or 8 years of experience with a Master's degree. Preferred qualifications: Master's degree in Statistics, Mathematics, Data Science, Engineering, Physics, Economics, or a related quantitative field. 12 years of experience using analytics to solve product or business problems, performing statistical analysis, and coding (e.g., Python, R, SQL). About the job The Education Data Science team plays a fundamental role in the success of Google for Education, by providing deep understanding of our business and product performance and developing insights to help drive strategic decisions. Further, as Generative AI continues to evolve and becomes integrated with Workspace Education products, you will be at these developments. You will help shape new product experiences and develop innovative ways to evaluate the quality of the content and the user experience. Individual pay is determined by factors including job-related skills, experience, and relevant education or training. US: $192000 - $279000 (USD) + 20% bonus target + equity + benefits Learn more about benefits at Google. Responsibilities Define, own and evolve product/business success metrics. Report, analyze and forecast trends of key product/business metrics and make recommendations to improve them. Lead the design, analysis, and interpretation of product experiments. Govern cross- product area resource allocation and technical rollout strategies by architecting rigorous predictive models and causal impact frameworks that protect critical engineering and infrastructure investments. Perform data exploration to understand user behavior and identify opportunities for improving and growing Education products. Identify critical business and product functionalities and assess their business impact. Apply technical expertise with observational data analysis, modeling, and causal inference to answer the most important product/business questions. Partner with Product, Engineering, UX and cross-functional teams to influence, prioritize and support product and business strategy.