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- Master's degree in Statistics, Data Science, Mathematics, Physics, Economics, Operations Research, Engineering, or a related quantitative field.
- 8 years of 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.
Preferred qualifications:
- 6 years of experience (e.g., statistician/bioinformatician/product analyst), including with statistical data analysis such as linear models, multivariate analysis, causal inference, sampling methods engagements outside class work at school can be included.
- Experience translating analysis results into business recommendations.
- Experience in articulating business questions and using mathematical techniques to find an answer using available data.
- Ability to select the appropriate statistical tools for a given data analysis problem, along with demonstrated effectiveness in both written and verbal communication skills.
- Ability to lead and take initiative, along with a demonstrated willingness to teach others and learn new techniques.
The US base salary range for this full-time position is $177,000-$266,000 + 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.
- Work with data sets. Solve difficult, non-routine analysis problems, applying advanced problem-solving methods as needed. Conduct analysis including data gathering and requirements specification, processing, analysis, 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.
- Interact cross-functionally with many people and teams. Work with engineers to identify opportunities for, design, and assess improvements to google products.
- Make business recommendations (e.g. cost-benefit, forecasting, experiment analysis) with presentations of findings at multi-level stakeholders through visual displays of quantitative information.
- Research and develop analysis, forecasting methods to improve the quality of Google's user facing products; examples include ads quality, search quality, end-user behavioral modeling, and live experiments.
Build for everyone Since our founding in 1998, Google has grown by leaps and bounds. Starting from two computer science students in a university... Read more