Top Mistakes to Avoid in Your 2023 Data Science Job Search

interview tips Feb 24, 2023

The unfortunate truth is that one mistake can ruin your chances of getting a job. That’s why in this post, we are going to go over the three most common mistakes people make during data science job searches.

Not only will I reveal what these mistakes are, but I will also teach you how to avoid them.

Before we start, know that if you have done these, don’t feel bad! These are the most common mistakes, so lots of people have done them. By the end of this post, you should know how to avoid making them again! You can also get this information from my video on this subject if you prefer. 

Mistake 1: Applying to Multiple Position Types at the Same Time

Submitting simultaneous applications for different position types is a mistake, even when those positions are all in the data science field.

It might seem like a good way to increase your chances, but the truth is that this will hurt rather than help. But why?

In short, it’s inefficient and ineffective.

Applying to every position you can reduces the amount of time you have to sharpen your skills. It takes a lot of time to fill out and tweak that many applications. There’s less overlap than you might think between positions, so each application will need to be adjusted extensively.

Then, what time you have left for studying will not be nearly enough to cover everything you need to know. Different data science positions require different skills. Trying to learn absolutely everything for any data science position is just not possible, especially not with limited time.

What all that means is that if you apply to a lot of different position types at once, there’s no way you will be adequately prepared for interviews. You’ll be pouring tons of time into applications and you’ll have too much to learn, which will likely lead to a low interview-to-offer rate.

Instead of doing that, what you should do is focus on one type of position (check out this post on to learn how to pick a position type). This will narrow down what you have to study and prepare so that you can spend more time honing your skills to be ready to ace interviews.

Mistake 2: Going into Interviews Unprepared

Going into interviews unprepared is far more common than you might think, and that’s because of a common misconception.

A lot of people assume that getting a job is a numbers game. If you apply to enough jobs, eventually you have to get one. That type of mindset leads to less focus on acing a particular interview.

Unfortunately, this just isn’t true. Numbers alone will not get you a job. You have to be able to stick the landing and get that offer, which means you have to be prepared for interviews. Let’s look at three examples of not being prepared to see what I mean.

Unprepared for Technical Interviews

For technical interviews, failing to practice is failing to prepare.

Take a coding interview for example. I’ve learned from my own experience that writing code with an interviewer looking over your shoulder is very different from writing it in a room by yourself. When you’re writing code in an interview you are expected to explain your thought process as you code without messing up the code.

That’s stressful and difficult, which is why practice is so important for passing technical interviews. Without that preparation, you could run out of time to solve the problems or be unable to explain what you are doing, which will prevent you from getting an offer.

Not Knowing Your Interviewer

Not knowing what to expect can really backfire, but a little research can go a long way.

For example, one time in preparing for a presentation I planned to discuss lots of technical details to impress my audience, whom I thought would be made up of fellow data scientists. However, the audience was actually made up of managers, and they were not interested in the technical details of the project. They wanted to hear about the business impact, and because I was not prepared to discuss that, the presentation went poorly.

You should always research your interviewer. A simple email can let you know at least the interviewer’s position so that you have an idea of what they will likely be interested in. Do some work so that you know what to expect when you walk into the interview.

Unfamiliar with the Product

If you want to work at a company, the company expects you to know what they do. If it becomes obvious that you have no idea what their product is or how it works, you will look unprepared and unmotivated.

I hope these three examples of what it means to be unprepared for interviews have convinced you that you always want to be well-prepared for interviews. It doesn’t matter how many interviews you have if you can’t turn any of them into offers.

Mistake 3: Paralysis by Analysis

The last mistake is something that might be especially tough for data scientists - paralysis by analysis.

Data scientists like to analyze things, but when it comes to job searching, you can take that too far. I meet a lot of people who spend so much time worrying about whether something will be helpful that they never take any action.

People want to know what will help them the most, and that’s understandable. But I also think that mindset can lead you to waste a lot of time worrying that you could be investing in new skills.

You don’t need to find the perfect resource that will teach you everything in an hour. Steady improvement will lead to solid abilities and successful interviews, and you’re only going to get there if you just start learning.

Take action and don’t overanalyze. You’ll never regret any investment in yourself.

Final Thoughts

If you’ve made any of these three mistakes, don’t be discouraged! I’ve made all of these at some point myself. I hope that you’ve learned something and that you have some lessons you can apply in the future.

If you want to read more, there’s a longer version of this post here.

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