Product Data Scientist, Analytics, Insights and Measurement

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.
  
• Experience with Google Ads products and technologies.
• Familiarity with privacy-preserving technologies and data regulations (e.g., GDPR, CCPA).
• Knowledge of machine learning techniques and their applications in advertising.
• Understanding of digital advertising measurement concepts, including attribution modeling, incrementality testing, and Media Mix Modeling (MMM).

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

• Execute defined, moderately difficult analytical tasks under guidance from the manager or team member/team lead. For straightforward problems, execute end-to-end analysis with minimal guidance.
• Collaborate with Product Managers, Engineers, and Research Scientists to define and track key performance indicators (KPIs) for Google Ads measurement products. 
• Communicate complex data and analysis in a clear and concise manner to various stakeholders, including product leadership, engineering teams, and business partners.
• Conduct in-depth analysis of large datasets to identify trends, patterns, and insights that inform product strategy and development.