Becoming a data scientist means getting through some tough interviews to work for an exciting company. Sometimes, the thought of facing the interview process alone can actually be enough to dissuade some candidates. However, while practice doesn't necessarily make perfect, it does help improve your confidence and teach you a few things you may not fully understand.
On the other side of the equation, if you're hiring someone, finding the right data scientist interview questions may prove difficult. Not only do you need to find someone who knows the technical side of things, but you need someone who shows intellectual curiosity, an understanding of how your business works, excellent communication skills, and the ability to work together with members of your digital marketing team and other people within your company.
The following questions can serve as practice for any aspiring data scientists and as inspiration for interviewers to ask when seeking new candidates.
1. When is the last time you had to choose between two types of analyses? Which one did you decide on and why?
2. What would you do if you found that data was missing or corrupted within a data set?
3. What are some of the most common methods of regularization?
4. Can you name a data scientist you admire? If not, what is your current favorite tech startup?
5. Name a real-world experience you've had that taught you lessons that you can apply to your career as a data scientist.
6. Why is data normalization important?
7. What are the basic steps to take when tackling any analytics project?
8. What are your favorite data-cleaning techniques?
9. Pretend your interviewer is a high-level executive at this company, and explain to them the importance of a lift.
10. Can you explain the difference between data science, machine learning, and artificial intelligence?
11. Can you recall a situation when you used logistic regression?
12. How would you handle a situation in which someone challenged your findings?
13. Imagine you have two sorted lists and you'd like to unite them into one. Write out a function that can do that.
14. Can you explain the curse of dimensionality?
15. Is an abundance of false negatives better or worse than an abundance of false positives?
16. If you had one week to prepare someone to become a data scientist, what subjects would you cover first?
17. Can you explain the role that finite precision has in data science and machine learning?
18. Why do you want to work as a data scientist at this company instead of others in this industry?
19. Explain the steps you would take to interpret logistic regression by using Microsoft Excel.
20. What is your preferred clustering algorithm, and what does a clustering algorithm do?
21. Name an example of when a false negative is more important than a false positive. Name an example of when a false positive is more important than a false negative.
22. What is selection bias, and why do you feel it's important to data science?
Practicing your answers to these questions, or the answers you're looking for as an interviewer, is a great way to prepare for the hiring process.