Business Data Scientist 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, making 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.
  
• Develop and automate reports, and iteratively build and prototype dashboards to provide insights at scale, solving for business priorities.
  
• Design and analyze controlled experiments or counterfactual causal inference studies to examine the incremental impact of business, product, or go-to-market initiatives. 

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 (i.e., linear models, multivariate analysis, stochastic processes, sampling methods, etc.).
  
• Experience with statistical software (e.g., R, Python, MATLAB) and database languages (i.e., SQL).
  

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.
  
• Experience leveraging data insights into storytelling for business stakeholders.
  
• Experience in controlled experiment design and causal inference methods.
  
• Ability to prioritize requests and partner well in an environment with competing demands from stakeholders.
  

• Currently pursuing a PhD degree in a quantitative discipline (e.g., statistics, biostatistics, physics, applied mathematics, operations research, economics).
  
• Experience with statistical methods (i.e., linear models, multivariate analysis, stochastic processes, sampling methods, etc.).
  
• Experience with statistical software (e.g., R, Python, MATLAB) and database languages (i.e., SQL).