Research Data Scientist, Core Metrics

Research Data Scientist, Core Metrics Share Research Data Scientist, Core Metrics _corporate_fare_ Google _place_ Mountain View, CA, USA **Mid** Experience driving progress, solving problems, and mentoring more junior team members; deeper expertise and applied knowledge within relevant area. Share Research Data Scientist, Core Metrics _info_outline_ XIn most instances, this position requires in-person interviews as part of the hiring process. **Minimum qualifications:**

• 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.

**Preferred qualifications:** + 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. **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 Discovery ads. These products - the heart of Google’s business driving $B+ in annual revenue - are complex, advanced, and they are rapidly growing and evolving. Individual pay is determined by factors including job-related skills, experience, and relevant education or training. US: $147000 - $210000 (USD) + 15% bonus target + equity + benefits Learn more aboutbenefits at Google ( . **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). Independently format, re-structure, and/or validate data to ensure quality, and review the dataset to ensure it is ready for analysis.

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