Qualifications

Minimum qualifications:

• Master's degree in Statistics, Economics, Engineering, Mathematics, a related quantitative field, or equivalent practical experience.
  
• 5 years of experience with statistical data analysis, data mining, and querying (e.g., SQL).
  
• 3 years of experience managing analytical projects.
  

Preferred qualifications:

• 4 years of experience as a statistician, computational biologist, bioinformatician, data scientist, or product analyst.
  
• Experience articulating business questions and using mathematical techniques to arrive at an answer using available data.
  
• Experience with statistical data analysis such as linear models, multivariate analysis, causal inference, and sampling methods.
  
• Experience translating analysis results into business recommendations.
  
• Experience in selecting the right statistical tools given a data analysis problem.
  
• Excellent written and verbal communication skills.
  

Summary

• Master's degree in Statistics, Economics, Engineering, Mathematics, a related quantitative field, or equivalent practical experience.
  
• 5 years of experience with statistical data analysis, data mining, and querying (e.g., SQL).
  
• 3 years of experience managing analytical projects.
  

Description

The Search Platforms Data Science (SPDS) team focuses on understanding and measuring Search systems. Data Scientists in Search Platforms 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, Site Reliability Engineers (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 $150,000-$223,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. The range displayed on each job posting reflects the minimum and maximum target salaries for the position across all US locations. 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. Solve difficult, non-routine analysis problems, applying advanced problem solving methods as needed. Conduct analysis that includes data gathering and requirements specification, processing, analysis, ongoing deliverables, and presentations.
  
• Build and prototype analysis pipelines iteratively to provide insights at scale. Develop comprehensive understanding of Google data structures and metrics, advocating for changes where needed for both products development and business activity.
  
• Work closely with engineers to identify opportunities for, design, and assess improvements to google products.
  
• Make business recommendations (e.g., cost-benefit, forecasting, experiment analysis) with effective presentations of findings at multiple levels of stakeholders through visual displays of quantitative information.
  
• Research and develop analysis, forecasting, and optimization methods to improve the quality of Google's user facing products (e.g., ads quality, search quality, end-user behavioral modeling, and live experiments).
  

Additional_instructions