Featurespace Mid-Level Data Scientist | January 2022
Location: Cambridge, United Kingdom
Role: Data Scientist
When did you have this interview?: Last month
It was though.
1- Tell me about yourself – up to 5mins
then technical questions started.
What kind of technical questions did you get asked?
2- Let’s talk about your recent project in which you used K-means clustering, could you explain how to evaluate your model?
3- How do you evaluate your model performance?
4- Could you explain to me one of your favourites / applied algorithms? And What are the main parameters of the method? (Decision Tree type method)
5- What are the differences between Random Forest and XGBoost?
6- What if training data performs perfectly, and test data fails, what is this situation called, explain it?
7- Once the overfitting happens which parameters would you tweak that will fix the problem on Decision Tree-based methods?
8- 5 Minutes CODING QUESTIONS (Explain at least pseudo-code style) - HIGH PACE- a. Given an array; we know it is incrementally increasing A= [1,2,5,9,13,17,21,25, 62, ….,1231] and we are looking specific number like x = 24. How do you write your method? (What would be your )
b. Do you know Big O Notation?
c. Imagine the previous array is a thousand times bigger; A.Shape = 1 x 1.000.000 How do you find your target this time x = 24293?
d. Ok, multiple your array 10 times so how can you handle the time difference for a 10.000.000 size array?
9- Last 5 MINUTES SEPERATED just for YOUR QUESTIONS. They didn’t tell anything until I ask a question