Senior Data Scientist, Research, Workspace

Minimum qualifications:

• Master's degree in Statistics, Data Science, Mathematics, Physics, Economics, Operations Research, Engineering, or a related field or equivalent practical experience.
• 5 years of experience in analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis, or 3 years of experience with an advanced degree.
• Experience with consumer-facing technical products.

Preferred qualifications:

• PhD in statistics, economics, math, or a related quantitative field.

At Google, data drives all of our decision-making. Quantitative Analysts work all across the organization to help shape Google's business and technical strategies by processing, analyzing and interpreting huge data sets. Using analytical excellence and statistical methods, you mine through data to identify opportunities for Google and our clients to operate more efficiently, from enhancing advertising efficacy to network infrastructure optimization to studying user behavior. As an analyst, you do more than just crunch the numbers. You work with Engineers, Product Managers, Sales Associates and Marketing teams to adjust Google's practices according to your findings. Identifying the problem is only half the job; you also figure out the solution.

The Workspace Data Science team empowers product teams for Drive, Docs, Sheets, Slides, and Vids through development of key metrics, data-motivated insights, modeling, experimentation, and other forms of causal inference to grow product adoption and AI usage.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.

• 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.
• Use custom data infrastructure or existing data models as appropriate, using knowledge. 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.