data science interviews, like other technical interviews, require plenty of preparation. There are a number of subjects that need to be covered in order to ensure you are ready for back-to-back questions on Statistics, programming and machine learning.
Before we get started, there’s one tip I’d like to share.
I’ve noticed that there are several types of data science interviews that companies conduct.
Some data science interviews are very product and metric driven. These interviews focus more on asking product questions like what kind of metrics would you use to show what you should improve in a product. These are often paired with SQL and some Python questions.
The other type of data science interview tends to be a mix of programming and machine learning.
We recommend asking the recruiter if you aren’t sure which type of interview you will be facing. Some companies are very good at keeping interviews consistent, but even then, teams can deviate depending on what they are looking for. Here are some examples of what we have noticed about some companies data science interviews.
Airbnb — Product heavy, metrics diagnostics, metrics creation, A/B testing, tons of behavioral questions and take home material.
Due to this variance, we’ve created a checklist to keep track of what subject areas you did study and what you still need to cover.
Let’s first start with making sure you can explain the basic data science algorithms.
At large tech companies, it is common to receive an occasional probability or Statistics question. While the questions won’t necessarily require complex math, if you haven’t thought about independent and dependent probabilities in while it is good to review setting up the basic formulas.
Most of these questions are either similar to the ones we have been asked or taken directly from glassdoor.com.
Statistics is a broad concept so don’t get too bogged down in the details of each of these videos.
