Minimum qualifications:
- Master's degree in Statistics, Data Science, Mathematics, Physics, Economics, Operations Research, Engineering, or a related quantitative field, or equivalent practical experience.
- 3 years of experience working with analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis, or a PhD degree.
- Experience with statistical data analysis and experimental design.
- Experience with regression analysis for prediction and forecasting.
- PhD in a quantitative discipline (e.g., Statistics, Operations Research, Bioinformatics, Economics, Computational Biology, Computer Science, Mathematics, Physics, or Engineering).
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About the job
The Ads Metrics team is the core Data Science team behind Google Search Ads. We develop mechanisms, experiment designs, evaluation metrics, statistical methods, and analysis libraries to help build the next generation of our Search advertising products.
As a Data Scientist in the Ads Metrics team, you will collaborate closely with software engineers, product managers, researchers, and analysts to push the boundaries of experiment design, causal inference, and time-series analysis for business-critical launches.
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.
Responsibilities
- Collaborate with stakeholders in cross-project 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). Independently format, re-structure, and/or validate data to ensure quality, and review the dataset to ensure it is ready for analysis.