Research Data Scientist, Search Ads

Minimum qualifications:

• Master's degree in Statistics, Data Science, Mathematics, Physics, Economics, Operations Research, Engineering, a related quantitative field, or equivalent practical experience.
• 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

In this role, you will work as part of the Ads Metrics Data Science team, supporting the SAGE (Search Ads and Google Experience) organization in developing Google's ad products, from classic text ads to shopping ads and new products like Discovery ads.

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.

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

• Apply quantitative knowledge to manage ambiguous problems, developing and applying advanced statistical and optimization models to find solutions.
• Design and develop methods and experiments to measure and analyze product quality and user behavior.
• Extract, manipulate, and analyze datasets using Python and SQL, with an emphasis on developing scalable Data Science solutions.
• Influence the strategy and direction of flagship products by translating questions into analysis, with a focus on longer-term solutions.