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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.
- 1 year of experience managing investigative projects.
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:
Our team is responsible for real-time bidding on behalf of Google Ads advertisers (i.e. GDA and AC). Our optimization modules oversee the transaction of ~20B ARR of ad inventory across web and app display publishers. We optimize bidding across web and app on several exchanges running different types of auctions.
As a Data Scientist, you will solve critical problems for the future of the display ads business. Our work involves data analysis, algorithms design/tuning, data pipelines design/implementation. You will have the opportunity to collaborate within the team as well as with other teams and build knowledge across the display ads ecosystem and the serving stack.
The US base salary range for this full-time position is $141,000-$202,000 + 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 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.
- Own and contribute to the bidding models that govern how Google Ads network bids on real-time auctions for display publisher inventory, across web and mobile properties.
- Improve the product by analyzing auction abuse patterns, duplicate queries, and designing solutions to counter them.
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