June 27, 2025

Senior Research Data Scientist, YouTube Search

Senior • On-site

$166,000 - $244,000/yr

San Bruno, CA , +1


Minimum qualifications:

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

In this role, you will develop statistical solutions to common problems faced by YouTube (YT) Search. The role also involves participating in experiments to understand issues, and contributing to foundational improvements in data quality.
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 business or product questions to answer. Provide feedback to translate business questions into analysis, evaluation metrics, or mathematical models.
  • Use custom data infrastructure or existing data models. Design and evaluate models to mathematically express and solve defined problems.
  • 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 tools (e.g., SQL, R, Python). Format, re-structure, or validate data to ensure quality, and review the dataset to ensure it is ready for analysis.