Staff Research Data Scientist, Search Ads, Ads Metrics

Minimum qualifications:

• Master's degree in Statistics, Data Science, Mathematics, Physics, Economics, Operations Research, Engineering, or a related quantitative field.
  
• 8 years of work experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis, or 6 years of work experience with a PhD degree.
  

Preferred qualifications:

• 10 years of work experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis, or 8 years of work experience with a PhD degree.
  

About the job

Ads Metrics is the Data Science team for Search ads. We support the SAGE (Search Ads and Google Experience) organization in developing the most important ad products at Google - from classic text ads, to rich shopping ads, to exciting new products like Demand Gen Campaigns, Local Ads, Travel Ads, etc. These products - the heart of Google’s business driving $200B+ in annual revenue - are complex, advanced, and are rapidly growing and evolving.

We guide product development and executive decisions with data. Particularly, we measure the long term impact of all the changes we make on users, advertisers, and Google systems with innovative experiment designs; identify new growth opportunities with deep data analysis; and understand the tradeoffs between user, advertiser and Google value with principled frameworks.

We work closely with our Eng/PM collaborators to improve our products, and with SAGE executives to understand the growth levers and their impact on business. Our role is to drive long term value for Google’s business with rigorous, insightful data science!

Responsibilities

• Collaborate with stakeholders in cross-projects and team settings to identify and clarify business or product questions to answer. Provide feedback to translate and refine business questions into tractable analysis, evaluation metrics, or mathematical models.
  
• Use custom data infrastructure or existing data models as appropriate, using specialized knowledge. Design and evaluate models to mathematically express and solve defined problems with limited precedent.
  
• Gather information, business goals, priorities, and organizational context around the questions to answer, as well as the existing and upcoming data infrastructure.
  
• Own the process of gathering, extracting, and compiling data across sources via relevant tools (e.g., SQL, R, Python). Format, re-structure, and/or validate data to ensure quality, and review the dataset to ensure it is ready for analysis.