- Work with large, complex data sets. Solve difficult, non-routine analysis problems, applying advanced analytical methods as needed. Conduct end-to-end 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 sales activity.
- Interact cross-functionally with a wide variety of people and teams. 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; example application areas include ads quality, search quality, end-user behavioral modeling, and live experiments.
- MS degree in a quantitative discipline (e.g., statistics, operations research, bioinformatics, economics, computational biology, computer science, mathematics, physics, electrical engineering, industrial engineering).
- 2 years of relevant work experience in data analysis or related field. (e.g., as a statistician / data scientist / computational biologist / bioinformatician).
- Experience with statistical software (e.g., R, Python, Julia, MATLAB, pandas) and database languages (e.g., SQL).
- PhD degree in a quantitative discipline as listed in Minimum Qualifications.
- 4 years of relevant work experience (e.g., as a statistician / computational biologist / bioinformatician / data scientist), including deep expertise and experience with statistical data analysis such as linear models, multivariate analysis, stochastic models, sampling methods. Analytical engagements outside class work while at school can be included.
- Applied experience with machine learning on large datasets.
- Experience articulating business questions and using mathematical techniques to arrive at an answer using available data. Experience translating analysis results into business recommendations.
- Demonstrated skills in selecting the right statistical tools given a data analysis problem. Demonstrated effective written and verbal communication skills.
- Demonstrated leadership and self-direction. Demonstrated willingness to both teach others and learn new techniques.