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Maca data scientist screener

Welcome to the take home technical screener! We want to see how you work in a real-world environment, rather than your ability to solve obscure algorithms. You have 3 days to complete the exercise. Once this is done, we’ll schedule a follow up session to pair on the application and talk through your work.

Prompt:

Using the customer churn data in this repo, please analyze and answer the following:

  • How did you approach cleaning the data?
  • What are the strongest indicators of what would cause a customer to churn?
  • What could a company do to reduce churn given this data?
  • If you had to retain customers, who would you prioritize to spend money on?

Provide a README of:

  • A reasonable decision log of what you think is important. We like to see thoughtful work.
  • Approximately how long did it take to build this solution

Please feel free to reach out with questions! We would rather spend a few minutes making sure you have a clear understanding of the problem set rather losing hours on a project that doesn't make sense

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