Senior Data Scientist, Research

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Company: Google

Location: Mountain View, CA 94040

Description:

Minimum qualifications:

  • Master's degree in Statistics, Data Science, Mathematics, Physics, Economics, Operations Research, Engineering, or a related quantitative field.
  • 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 3 years of work experience with a PhD degree.


Preferred qualifications:

  • 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


About the job

The team is responsible for bidding on behalf of Google Ads advertisers. The optimization modules oversee the transaction of ad inventory across web and app display publishers. In this role, you will work with data analysis, algorithms design/tuning, data pipelines, and design/implementation. You will have the opportunity to mentor/lead other data scientists in the team with cross-functional collaborations and will build knowledge across the Display ads ecosystem.

The US base salary range for this full-time position is $166,000-$244,000 bonus equity benefits. Our salary ranges are determined by role, level, and location. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your preferred location during the hiring process.

Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits. Learn more about benefits at Google .

Responsibilities

  • Collaborate with stakeholders in cross-projects and team settings to identify and clarify business or product questions, and provide feedback to translate and refine business questions into analysis, evaluation metrics, or mathematical models.
  • Use custom data infrastructure or existing data models, design and evaluate models to express and solve defined problems.
  • Own the process of gathering, extracting, and compiling data across sources via tools, and format, re-structure, or validate data to ensure quality, and review the dataset to ensure it is ready for analysis.
  • Own and contribute to the strategy of the bidding models that govern how the ads network bids on real-time auctions for display publisher inventory, across web and mobile properties
  • Analyze auction abuse patterns, duplicate queries, and design solutions to counter them.

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