Stripe is hiring a

Data Scientist

San Francisco, United States

Develop forecasts, quantify risk, and generate insights from data.

Stripe is the best software platform for running an internet business. We handle billions of dollars every year for hundreds of thousands of businesses around the world. One third of Americans bought something on Stripe in the last year.
With all this data, we’re looking for talented data scientists to join the Applied Machine Learning group to help us better understand our users and build better financial products. If you are an expert working with data to forecast performance and risk, and excited to apply your experience to build new businesses based on unique data sets, we want to hear from you.
Guide to the onsite interview (PDF)

You will

  • Generate the forecasting methodology, risk controls, and product insights necessary to support new financial products and services Stripe will offer its users
  • Apply statistical and econometric models on large datasets to (1) measure results and outcomes, (2) identify causal impact and attribution, and (3) predict future performance of users or products
  • Design, analyze, and interpret the results of experiments
  • Drive the collection of new data and the refinement of existing data sources
  • Work closely with product, business, and engineering teams to use the results of your analyses and forecasts to launch new products

You'd ideally have

  • A Ph.D. or M.S. in a quantitative field (including, but not limited to, economics, mathematics, statistics, and computer science)
  • Expert knowledge of a scientific computing language (such as Python or R) and SQL
  • Strong knowledge of statistics and experimental design
  • Extensive experience in credit analysis and underwriting (experience in forecasting and pricing is a plus)
  • Experience with tools for working with “big data” in a distributed fashion (Spark, Hadoop, Pig, etc.)
  • The ability to communicate results clearly and a focus on driving impact

You should include these in your application:

  • A resume and LinkedIn profile
  • A description of the most interesting data analysis you’ve done, the key findings, and the impact of the analysis
  • A sample of code you've written related to data analysis

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