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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:
As a part of the Search Intelligence Data Science team, you will help leverage data, measurement methodologies, analysis insights and modeling to help improve the quality of Search AI product in the context of latest GenAI capabilities and unlock new ways/use cases on how billions of users interact with Google search.In this role, you will have an exciting opportunity to work on the fast evolving AI feature of Google Search. You will be working closely with a strong team of developers and product managers, and delivering on a roadmap aimed at revolutionizing the Google Search experience with the latest GenAI model capabilities. You will design and develop measurement approaches to make sure we deliver the most useful product to users. Your insights will help voice user needs, unlock new use cases and ways of how billions of users interact with Google search on a daily basis.
Individual pay is determined by factors including job-related skills, experience, and relevant education or training. US: $207000 - $300000 (USD) + 20% bonus target + equity + benefits
Learn more about benefits at Google.
Responsibilities:
- Lead and drive cross-functional development with a wide variety of teams, working closely with Product Managers and Engineers to identify growth and quality opportunities for the product.
- Drive understanding, evaluation and development of measurement methodologies for complex GenAI models.
- Conduct rigorous analyses of large-scale datasets, applying statistical methods to solve complex problems, and presenting actionable recommendations to various stakeholders as your insights will influence product development, improve user experiences, and drive business outcomes.
- 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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