Staff Product Data Scientist, Pixel Growth, Google Store

Google Store is the first-party e-commerce site for Google's expanding portfolio of hardware products and associated services. As the authoritative place to learn about and purchase Google's hardware, our product range includes Pixel phones, Pixel Watch, Fitbit, Google Home and Health products, and accessories. The data science team drives business and product decisions through measurement, experimentation, modeling, causal inference, and optimization.

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.

• Perform analysis utilizing relevant tools (e.g., SQL, R, Python) and provide investigative thought leadership through proactive and strategic contributions.
• Develop the data science roadmap that delivers against the broader strategy. Establish project goals, coordinate resources, and provide technical leadership.
• Synthesize insights from cross-channel experimentation, calculating price elasticity curves and establishing performance baselines for user interventions.
• Define and report Key Performance Indicators (KPIs) during business reviews. Translate analysis results into business insights or product improvement opportunities.
• Build predictive models integrating complex datasets and serve as the subject matter expert driving metrics development, modeling, and presenting to stakeholders.

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.
• Experience with commercial metrics or business models (e.g., e-commerce, retail, subscriptions, promotions, or pricing).

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).
• Experience building predictive models for customer lifetime value (LTV), pricing, or user churn.