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Tech Challenge for Data Scientist Position

Welcome to our Data Science Tech Challenge.

In this assessment, your mission is (if you accept the challenge) to train a model for predicting credit outcomes. More specifically, we would like you to train a classifier to decide if it is a good idea to give a credit to a customer (in that case the customer is called a good customer) or not (a bad customer).

Since we are great Python lovers, we ask you to implement your approach in Python. Of course, you can make use of popular python tools such as pandas, pySpark and scikit-learn. If you prefer any other Python tool, please explain why. Finally, so we can have a better understanding of your approach and ideas as well as problems you might have faced, please comment your code.

What is the mission?

As a warm-up, in Task A we ask you to get familiar with the data set. The actual challenge takes place in Task B: solving an inverse problem via machine learning or statistical techniques. Since we don't want you to spend days or weeks on completing the assessment, next we let you decide if you want to complete the assessment by either solving Task C or Task D: Task C is about the overall optimization and Task D is about putting your code in production. Of course, we won't keep you from solving both :-)

Task A. Looking at the Data

Inside this repository, you'll find a file containing training data as well as a description of the file format:

  1. Load the data and replace codes with more meaningful values.

  2. Visualize the distribution of the attributes inside the data. Do you see anything interesting there?

Task B. Predicting Credit Outcomes

  1. Train two different models that can predict the result (good vs. bad) for a given credit based on the attributes of the credit/person.

  2. Compare them and explain the outcomes of these models. Which one is better than the other one and why? Which features are most important for your model’s decision?

Task C. Dealing with the Risk

Consider the following:

  • giving out a credit that turns out to be good will give you 30% profit of the credit sum.
  • giving out a credit that turns out to be bad will cost you 90% of the credit sum.
  • losing a good customer will give you a bad reputation resulting in 5% of the credit sum in future loss.
  • rejecting a bad customer is the right thing to do and results in no loss at all.

  1. How can you apply your model to give an optimal recommendation for approving credits under these conditions?

  2. What is the expected profit when using the optimized model?

Task D. Automation

  1. Create a Python script that applies your credit outcome prediction on a given input file. Your output file should contain the credit identifier and your models assessment of the credit (good vs. bad).

  2. Create an airflow DAG that is able to apply your credit outcome prediction in a daily manner and test it using our supplied airflow docker images.

    You'll first have to initialize airflow with docker-compose run airflow-init and then can start everything up with docker-compose up. After a few minutes, you should be able to log into http://localhost:8080 using airflow:airflow as credentials. For more documentation on running airflow inside docker, please visit the official documentation.

For testing your script and airflow DAG, you can use the credit-input-20150314 input file.

And now what?

Please send your solution to [email protected] and we will get back to you asap. Please do not create a pull request or fork this repository as your solution should not be end up being public afterwards.

Good luck and happy coding!