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Senior Staff Data Scientist, Research, Search Platforms

Google
Full-time
On-site
Mountain View, California, United States
$248,000 - $349,000 USD yearly
Data Science
Note: By applying to this position you will have an opportunity to share your preferred working location from the following: Mountain View, CA, USA; Cambridge, MA, USA.

Minimum qualifications:

  • Master's degree in Statistics, Data Science, Mathematics, Physics, Economics, Operations Research, Engineering, or a related quantitative field.
  • 10 years of experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis, or 8 years of experience with a PhD degree.

Preferred qualifications:

  • PhD degree in a quantitative field like engineering, computer science, mathematics, statistics or economics or in business science.
  • 12 years of experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis, or 10 years of experience with a PhD degree.
  • Ability to lead and self-direct effectively.
  • Ability to both teach others and readily learn new techniques.
  • Excellent skills in selecting the right statistical tools for data analysis problems.
  • Excellent written and verbal communication skills.


About the job

Search Platform Data Science (SPDS) team works on understanding and measuring Search systems, including but not limited to Experiment, Reliability, Velocity, Latency, Capacity, Content and Ecosystem, Logs Quality and Search Infrastructure. Search Platforms Data Science team work on understanding and measuring Search systems.

In this role, you will work on defining and developing the system metrics that would let us understand how effective and efficient our systems are, and how they interact with each other (and other Google systems). The SPDS team partners with engineering and product teams in Search Platforms on system-level optimizations, and collaborates closely with Search verticals and horizontal teams, SREs, Core to understand the impact of system changes on user experience and on Google business.In Google Search, we're reimagining what it means to search for information – any way and anywhere. To do that, we need to solve complex engineering challenges and expand our infrastructure, while maintaining a universally accessible and useful experience that people around the world rely on. In joining the Search team, you'll have an opportunity to make an impact on billions of people globally.

The US base salary range for this full-time position is $248,000-$349,000 + 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

  • Work with large, complex data sets to solve difficult, non-routine analysis problems using advanced analytical methods. Conduct analysis, from data gathering to presentations.
  • Build and prototype analysis pipelines for scalable insights. Develop an understanding of Google data structures and metrics, advocating for changes to support product development and business.
  • Interact cross-functionally with teams and individuals. Collaborate closely with engineers to identify, design, and assess improvements for Google products.
  • Make business recommendations through effective presentations to stakeholders, leveraging visual displays of quantitative information for cost-benefit, forecasting, and experiment analysis.
  • Research and develop analysis, forecasting, and optimization methods to enhance Google's user-facing products, including areas like ads and search quality, user behavioral modeling, and live experiments.