Data Scientist, Research Intern, PhD, Summer 2027

• 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.).