US: $118000 - $157000 (USD) + 0% bonus target
Learn more about benefits at Google.
Work with large complex data sets, solve difficult non-routine analysis problems, and apply advanced analytical methods. 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 a comprehensive understanding of Google data structures and metrics, advocating for changes.
Interact cross-functionally with a wide variety of people and teams. Work closely with engineers to identify opportunities, 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 products.
Minimum qualifications
Currently pursuing a PhD degree in a quantitative discipline (e.g., statistics, biostatistics, physics, applied mathematics, operations research, economics).
Experience with statistical methods (linear models, multivariate analysis, stochastic processes, sampling methods, etc.).
Experience with data analysis.
Experience using R or Python and technology to work with datasets such as scripting or statistical software (R, SAS, etc.).
Preferred qualifications
Currently attending a degree program in the US and available to work full time for 12 weeks outside of university term time.
In their penultimate academic year or returning to a degree program after completion of the internship.
Ability to draw conclusions from data and recommend actions.
Ability to take on a leadership role and take initiative.
Interest in learning new techniques.
Currently pursuing a PhD degree in a quantitative discipline (e.g., statistics, biostatistics, physics, applied mathematics, operations research, economics).
Experience with statistical methods (linear models, multivariate analysis, stochastic processes, sampling methods, etc.).
Experience with data analysis.
Experience using R or Python and technology to work with datasets such as scripting or statistical software (R, SAS, etc.).
