Job Description
As a Senior Data Scientist, Ads Metrics, you will collaborate with software engineers, product managers, researchers, and analysts to innovate and enhance experiment design, causal inference, and time-series analysis for business-critical launches. You will be responsible for identifying and clarifying business or product questions, translating those questions into analysis, evaluation metrics, or mathematical models, and gathering, extracting, and compiling data across sources via relevant tools. You will also own the process of validating data quality and ensuring that the dataset is ready for analysis.
Good To Have:
- Master's degree or PhD in a quantitative discipline (e.g., Statistics, Operations Research, Bioinformatics, Economics, Computational Biology, Computer Science, Mathematics, Physics, or Engineering).
- 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.
- Experience with regression analysis, experiment design, sampling methods, causal inference, time-series analysis, and hierarchical models.
- Experience in data analysis to solve business problems in complex business environments.
- Excellent scientific writing, communication, and presentation skills.
Must Have:
- Master's degree in Statistics, Data Science, Mathematics, Physics, Economics, Operations Research, Engineering, or a related quantitative field or equivalent practical experience.
- 5 years of experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis, or 3 years of work experience with a PhD degree.
- Experience with statistical data analysis and experimental design.
- Experience with regression analysis for prediction and forecasting.
Minimum qualifications:
- Master's degree in Statistics, Data Science, Mathematics, Physics, Economics, Operations Research, Engineering, or a related quantitative field or equivalent practical experience.
- 5 years of experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis, or 3 years of work experience with a PhD degree.
- Experience with statistical data analysis and experimental design.
- Experience with regression analysis for prediction and forecasting.
Preferred qualifications:
- Master's degree or PhD in a quantitative discipline (e.g., Statistics, Operations Research, Bioinformatics, Economics, Computational Biology, Computer Science, Mathematics, Physics, or Engineering).
- 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.
- Experience with regression analysis, experiment design, sampling methods, causal inference, time-series analysis, and hierarchical models.
- Experience in data analysis to solve business problems in complex business environments.
- Excellent scientific writing, communication, and presentation skills.
About the job
In this role, you will collaborate with software engineers, product managers, researchers, and analysts to to innovate and enhance 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-projects and team settings to identify and clarify business or product questions to answer. Provide feedback to translate and refine business questions into analysis, evaluation metrics, or mathematical models.
- Use custom data infrastructure or existing data models as appropriate, using knowledge. Design and evaluate models to mathematically express and solve defined problems.
- 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, or validate data to ensure quality, and review the dataset to ensure it is ready for analysis.
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