The loan repayment challenge. Build models to predict loan risk or quality (loan repayment).
Data Scientist Interviews
Data Scientist Interview Questions
In a data scientist interview, expect employers to ask questions that assess your data modeling, problem-solving, and programming skills. Be prepared to answer general questions that test your knowledge of statistics and data science. You should also be ready to answer open-ended questions that test your creativity, communication skills, and formal education in data modeling and programming.
Top Data Scientist Interview Questions & How to Answer
Question 1
Question #1: Which data modeling techniques do you prefer and why?
How to answer
How to answer: Turning data into understandable and actionable information is a critical part of the data scientist's job. This question allows employers to understand your data modeling skills and background. List and discuss your preferred data modeling techniques, including benefits such as ease of use, flexibility, etc.
Question 2
Question #2: How would you detect bogus Instagram accounts used for scamming consumers?
How to answer
How to answer: Questions like this one allow an employer to test your problem-solving skills. When answering open-ended questions such as these, feel free to ask clarifying questions and use whiteboards to demonstrate your coding and diagramming skills. Share your thought process as you work through the problem.
Question 3
Question #3: Describe circumstances that require a list, tuple, or set in Python.
How to answer
How to answer: Interviewers will use questions such as this one to test your Python programming skills. Review Python basics such as lists, tuples, and sets before your interview. You should be able to explain when and how each tool is used by data scientists.
54,359 data scientist interview questions shared by candidates
Walk through a bayesian probability question theoretically then code in Python.
About yourself
Tell me about the projects you have worked
In phone screen, how would you calculate the posterior probability in a dice rolling experiment with weighted dice?
They asked a lot of questions like machine learning algorithms, basic statistics, knowledge of R and Python and also about projects you did with your current employer.
walk us through your profile
Case study: How will you allocate resources in a call center? What do we learn from demand and supply? Will go after precission or Recall for such a case as a check on your model?
Describe how do you use CNN as your project? How difficult it is to explain the feature importance in deep neural networks.
DS challenge, is more like an unsolvable problem
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