Data Scientist, Research, Storage Analytics

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:

• 5 years of experience in scripting, statistical analysis [e.g., R, Stata, Statistical Package for the Social Sciences (SPSS), Statistical Analysis System (SAS), developing and managing metrics, and evaluating programs/products.
• 3 years of experience working in a changing organization.
  
• 1 year of experience preparing and delivering technical presentations to executive leadership.
  

The team's goal is for Storage to make optimal use of evolving user demands and hardware through resource abstraction, policy direction, system simplification, and algorithmic optimization.

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.

• Work with Engineering teams and other Data Scientist to provide strategic insights and direction, build probabilistic models of the systems, and compare those models with production to identify opportunities.
• 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.
• Design and evaluate models to mathematically express and solve defined problems with limited precedent.
• Gather information, business goals, priorities, and organizational context, as well as the existing and upcoming data infrastructure.
• Own the process of gathering, extracting, and compiling data across sources through tools (e.g., SQL, R, Python), and format, re-structure, or validate data to ensure quality, and review the dataset to ensure it is ready for analysis.