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Interview process for ML Engineers and Data Scientists:
Screening
Machine Learning
Coding
Case studies
System design
Behavioral
Here's what you can expect at each step (Thread)
Screening Machine Learning Coding Case studies System design Behavioral Here's what you can expect at each step (Thread)
Machine Learning Usually theoretical questions:
Linear models L1 vs L2 regularization XGB vs Random Forest Why need activation for neural nets
CodingUsually, it's leetcode-style questions (easy and medium)
Run-Length Encoding Find K largest elements in a list Find K most common words in a textAdditionally, you may expect SQL questions
Case studiesGiven some requirements, translate a business problem into ML terms:
How would you solve the customer churn problem? How would you find promising leads? How would you predict the prices of items?
System designDesign an end-to-end system for solving:
Spam detection Serving deep learning models Autocomplete in search
Behavioral Questions like "tell me about a time when you ..."
Disagreed with your manager Were wrong Missed a deadlineOr simply questions about your experience
Projects you were in Your role in these projects
ScreeningThe screening is usually a combination of
Behavioral Machine Learning Coding
This repo might be quite helpful for preparing for ML and data science interviews: https://github.com/alexeygrigorev/data-science-interviews
Disclaimer: This thread doesn't necessarily reflect the interview process at OLX
