Data Scientist - Product Analytics

Data Scientist, Product, Google Play Games

Minimum qualifications:

• Bachelor's degree in Statistics, Mathematics, Data Science, Engineering, Physics, Economics, or a related quantitative field.
• 5 years of work experience with analysis applications (extracting insights, performing statistical analysis, or solving business problems), and coding (Python, R, SQL) (or 2 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.
• 5 years of work experience with analysis applications (extracting insights, performing statistical analysis, or solving business problems), and coding (Python, R, SQL).
• Experience in experimental design (e.g., A/B, multivariate) and incremental analysis.
• Experience working with multiple datasets/data warehouses.
• Ability to pull from data sets using tools and coding.

About the job

Help serve Google's worldwide user base of more than a billion people. Data Scientists provide quantitative support, market understanding and a strategic perspective to our partners throughout the organization. As a data-loving member of the team, you serve as an analytics expert for your partners, using numbers to help them make better decisions. You will weave stories with meaningful insight from data. You'll make critical recommendations for your fellow Googlers in Engineering and Product Management. You relish tallying up the numbers one minute and communicating your findings to a team leader the next.

Responsibilities

• Design, execute, and interpret complex A/B tests, and multivariate experiments to validate product hypotheses and measure long-term user impact.
• Define and track the key performance indicators (KPIs) that measure product health, such as user engagement, and ecosystem health.
• Partner directly with Product Managers (PMs), Engineers, and UX Researchers to translate raw data into actionable insights and strategic recommendations.
• Build and iterate on automated analysis pipelines and interactive dashboards to democratize data access and provide scalable insights to the organization.
• Apply advanced statistical techniques (e.g., regression, time-series forecasting, and causal modeling) to identify growth opportunities and predict the impact of future features.

Experience Level

Mid Level