The Non-Company Specific Approach to Data Science Interview Preparation

interview tips Feb 24, 2023

There’s a lot you need to know to pass all the various stages and interviews to finally land your data science dream job. It can be overwhelming trying to prepare and even just trying to figure out exactly what you need to know.

To help make things a bit more manageable, we’re going to cover two things in this post: the types of interviews you will encounter in a data science job search and my recommended non-company-specific approach for preparing efficiently and effectively.

Knowing both what to prepare for and how to prepare will help you get ready to ace your next opportunity.

Before you read any more, remember that you can also head to my Youtube channel and check out the corresponding video on this subject. 

Now, let’s dive in!

Types of Interviews for Data Scientists

What can you expect if you’ve made it to the interview stage for a data science position?

The interview rounds typically consist of 1 to 2 rounds of technical phone screening and 4 to 6 rounds of onsite interviews. Some companies also have online assessments and take-home assignments.

That’s a lot and why you need to be familiar with the six types of interviews. You likely will not encounter all of them at a single company, but you will definitely have to pass several and that takes knowing what to expect. That’s why we’re going to look at how each interview is designed and how they evaluate candidates.

The Product Case Interview

Product case interviews (also known as the metric or business case interview) test your product knowledge and critical thinking. They are especially important for analytics-driven positions.

In this interview, you will be given a business scenario or problem and asked to make recommendations. You need to be able to show an ability to design metrics, diagnose metric shifts, and have ideas about product improvement and business problems. A knowledge of A/B testing is also important.

To learn more about preparation, check out this playlist of videos on the subject.

SQL Interview

SQL is part of your daily job as a data scientist, so this interview is crucial.

You need to demonstrate proficiency by showing familiarity with SQL language, syntax, and function. You also need to show logical thinking and the ability to work efficiently with limited time. You can find sample questions to practice for this interview on LeetCode and Hackerrank.

Probability and Statistics Interview

Probability and statistics interviews are very technical. They evaluate your knowledge of applied statistics and probability, which are necessary skills to perform the job of a data scientist. The questions can both cover concepts and have you performing actual calculations.

To learn more about this type of interview, I recommend checking out my YouTube playlist on the topic.

The Machine Learning Interview

This type of interview is especially important for algorithm-driven positions. You will need to demonstrate an understanding of machine learning models and how to use them to deal with data.

To learn more about these interviews, I recommend this post and this playlist.

The Coding Interview

As you can probably guess, coding interviews test your coding ability, including proficiency in programming or scripting languages like Python. You also need to show computer science fundamentals and an understanding of algorithms and data structures.

Remember that coding and SQL are not the same thing. Coding interviews require you to code up algorithms and data structures. However, just like SQL interviews, in coding interviews, you need to show a logical understanding of problems and come up with efficient solutions in a limited amount of time. You can also find example problems on LeetCode.

The Behavioral and Experience Interview

This interview asks what you would do in hypothetical situations and what you have done in the past. The goal of this interview is to see if you fit the company culture.

Do not underestimate this interview! It may not be technical, but more tech companies are putting emphasis on these interviews in their hiring process.

For more information on behavioral interviews, I have another playlist with all you need to know.

What Interviews Should I Focus On?

Six interviews are still a lot, which is why I want to remind you that you most likely will not encounter all of them. The type of position you're targeting will determine which interviews you should focus on. If you don’t know what position you’re targeting, check out this helpful post.

For analytics-driven roles, you should prioritize the product case and SQL interviews.

For algorithm-driven roles, coding and machine learning interviews should be your focus.

The probability and statistics and behavior and experience interviews are important for both tracks.

The Benefits of a Non-Company-Specific Approach

Even knowing you don’t have to focus on all six, you may still be feeling stressed. There’s still a lot to do. How do you even approach such a large task?

I highly recommend using a non-company-specific approach to prepare which means focusing on building a strong foundation and working on fundamental knowledge before interview questions.

Why do I recommend this method? Let’s look at the alternative first - the company-specific approach. This preparation method involves looking up sample interview questions online for a company. Many people use this method.

However, it has several large problems. With the company-specific approach, you cannot start preparing until you know what company you are interviewing with. This gives you very little time to cover quite a lot of information.

Also, this method is very rigid. It relies on you memorizing questions, and if you get different questions in the interview you will not be able to adapt your knowledge easily. The rigidity also means that you have to start from scratch every time you get interviews with a new company.

The non-company-specific approach solves both of these problems. It gives you a deep flexible understanding and is actually more efficient.

Instead of waiting till you land interviews and trying to cram and memorize a bunch of questions, the non-company-specific approach means you can start before you even get interviews and you can be preparing for multiple companies at once. This is overall far more efficient than the company-specific approach.

Focusing on the fundamentals also means that you will be learning rather than memorizing. You will be far more comfortable with the material, and you won’t have to relearn things for every interview. You can build on your knowledge over time.

The non-company-specific approach may feel like a lot to study, but I believe that in the long run, a more general approach will save you time and help you be better prepared.

Final Thoughts

With six types of interviews, cramming is never a good idea. Get started studying now with a general approach, and if you want study tips to make the most of your time be sure to check out this post.

If you want to read a longer version of this post complete with example questions for all the interviews, you can go here.

Effortlessly learn data science and prepare for data science interviews with our free, organized resources.
Download All Resources Now!