Senior Product Data Scientist, Education

Minimum qualifications:

• Bachelor's degree in Statistics, Mathematics, Data Science, Engineering, Physics, Economics, or a related quantitative field.
  
• 8 years of work experience using analytics to solve product or business issues, performing statistical analysis, and coding (e.g., Python, R, SQL) (or 5 years work experience with a Master's degree).

Preferred qualifications:

• Master's degree in Statistics, Mathematics, Data Science, Engineering, Physics, Economics, or a related quantitative field.
  
• 8 years of work experience using analytics to solve product or business issues, performing statistical analysis, and coding (e.g., Python, R, SQL).

About the job

Google for Education develops educational products, platforms, and business solutions to support teaching and learning globally by bringing Google technology to students, teachers, and education leaders. In this role, you will help grow these products and platforms.

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.

As Generative AI continues to evolve and becomes integrated with Workspace Education products, you will contribute to these developments. You will help shape new product experiences and develop innovative ways to evaluate the quality of the content and the user experience.

Responsibilities

• Build and manage relationships with investigative, engineering, and product leaders in partner product areas (PAs) to align on metric instrumentation, resolve data dependencies, and co-develop analyses that influence shared product roadmaps and resource allocation.
• 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.
• Apply technical expertise with observational data analysis, modeling, and causal inference to answer the product/business questions.
• Architect unified data pipelines and single-source-of-truth measurement frameworks by integrating cross-PA data sources to map end-to-end user journeys and evaluate cross-product performance.
• Deliver effective presentations of data-driven insights and recommendations to multiple levels of stakeholders.