Research Data Scientist, Search Ads, Google Experience

Google•Mountain View, CA

21h•$147,000 - $211,000

About The Position

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 Discovery ads. These products - the heart of Google’s business driving $100B+ in annual revenue - are complex, advanced, and they are rapidly growing and evolving. Google Ads is helping power the open internet with the best technology that connects and creates value for people, publishers, advertisers, and Google. We’re made up of multiple teams, building Google’s Advertising products including search, display, shopping, travel and video advertising, as well as analytics. Our teams create trusted experiences between people and businesses with useful ads. We help grow businesses of all sizes from small businesses, to large brands, to YouTube creators, with effective advertiser tools that deliver measurable results. We also enable Google to engage with customers at scale. Individual pay is determined by factors including job-related skills, experience, and relevant education or training.

Requirements

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

Nice To Haves

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

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, or validate data to ensure quality, and review the dataset to ensure it is ready for analysis.