7 Reasons Why You're Struggling To Land A Data Science Job

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7 Reasons Why You're Struggling to Land a Data Science Job
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Tired of applying to data science roles and not hearing back from companies? Perhaps you managed to land a couple of interviews but weren’t able to convert them to offers? Well, you’re not alone.

The job market is brutally competitive now. So just because it’s difficult doesn’t mean you’re not good enough. That said, it’s both important and helpful to take a step back and see how and where you can improve. And that’s exactly what this guide will help you with.

We’ll go over common reasons why aspiring data professionals like you struggle to make the cut. And how you can improve your chances of landing interviews and getting that job you want!

It’s a hard truth. So let’s face it.

Say you’ve applied to a bunch of data science roles at companies that you’re interested in. And have been shortlisted for interviews.

Congratulations! You’re on the right track. The next goal is to convert the interview opportunity to a job offer. And the first step is to crack that coding interview.

You’ll first have a round of timed coding interviews—testing your problem-solving skills—followed by an SQL coding round.

But coding interviews are difficult to crack—even for experienced professionals. But consistent practice and spaced repetition can help you successfully crack these interviews.

Regularly practice coding interview questions on platforms like Leetcode and Hackerrank.

If you are looking for resources check out:

Once you clear coding interviews, focus and prepare for technical rounds. Brush up your machine learning fundamentals. Also review your projects so you can explain their impact with confidence.

It is true that recruiters spend only a few seconds reviewing your resume and decide if it proceeds to the next phase or to the reject pile.

7 Reasons Why You're Struggling to Land a Data Science Job
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So you should put in conscientious efforts to draft your resume. Be sure to tailor your resume based on the job specifications.

Here are a few resume tips:

Include relevant experiences and education sections.
List experiences and education and reverse chronological order.
Summarize experience in bulleted lists—quantifying impact and adding concise explanations.
Include a relevant projects section. Explain the projects in concise bullet points. Also include links to the projects.
Add a relevant skills section grouped by category like programming languages, tools and frameworks etc.

I’ll also suggest using a simple single-column layout that’s easier to parse than complicated and fancy layouts.

When you’re applying to jobs, your resume and LinkedIn profile should be consistent without any conflicting details. And they should also be aligned with the experience and skill set that the role demands.

There are a couple of caveats you should avoid, though.

Your Profile Is Too Specific

Suppose you’re interested in medical imaging and computer vision. So almost all your projects are in computer vision. Such a profile may be a great fit for a computer vision engineer or a computer vision researcher role.

But what if you’re applying to a data scientist role at a FinTech company? Clearly, you don’t stand out as a strong candidate.

Your Profile Is Too Generic

If you are an aspiring data scientist with strong SQL skills and experience building machine learning models, you can apply for the roles of data analyst and machine learning engineer as well.

But you don’t want to make your resume/candidate profile look like you’re someone who wants to be a data analyst, a machine learning engineer, and a data scientist—all at once.

If you’re interested in all of these roles, have separate resumes for each.

It’s important to find a sweet middle ground that allows you to showcase your expertise and stand out as a potential candidate with a broad skill set that is aligned with the job’s requirements.

Your projects help you gain a competitive edge over other candidates. So choose them wisely.

7 Reasons Why You're Struggling to Land a Data Science Job
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Some aspiring data professionals put on their resume and portfolio certain projects which they shouldn’t be. Yes, there are some beginner projects which are good for learning—but you should AVOID showcasing them in your portfolio.

Here are a few:

Titanic survival prediction
MNIST handwritten digit recognition
Classification using the iris dataset
Projects on the wine dataset

Just to name a few. These projects are too generic and basic to be able to land you an interview (let alone job offers).

So what are some interesting projects—especially if you are a beginner who is looking to break into this field?

Here are some beginner-level projects that would help you showcase your skills and emerge as a stronger candidate:

Use real-world datasets to build your projects. This way you can showcase a lot of important skills: data collection, data cleaning, and exploratory data analysis besides model building.

Also include projects that are inspired by your interest. As I’d suggested in a previous pandas guide, try turning data from your interests and hobbies into interesting projects that will help you leave an impression on the interviewer.

Another common road block aspiring data professionals face is their educational background. Breaking into data science can be especially difficult if you have majored in a field such as sociology, psychology, and the like.

While your skills—hard and soft skills—matter eventually, you should remember that you are competing with those who have an undergraduate or advanced degree in a related field.

So what can you do about this?

Look for ways to constantly upskill yourself. Remember, once you land your first data role, you can leverage your experience going forward.

Look for ways to work on relevant projects within your company. If your company has a dedicated data team, try to accept a small side project.

Learning in public is super important, especially when you are trying to land your first job (and even after that, honestly).

I started writing online in late 2020. Since then, I’ve landed most of my opportunities through my work—tutorials and technical deep dives—that I published online.

So how and where do you start? Leverage social media platforms like LinkedIn and Twitter (X) to share your work with the community:

Built a project? Share it with your network. Ask for feedback. Improve.
Wrote a data science tutorial? Share it with your network.
Learned something new? Share it anyway.
Ran into an error that you eventually fixed? Yes, it’s worth sharing.

What you code on your laptop stays on your laptop. So be ready to put yourself out there and share what you build and learn.

Building a strong portfolio and online presence can be immensely helpful in the job search process. Because you never know which project or article might interest your future employer.

Because of how competitive the job market is right now, you have to go beyond just applying to jobs—and start being more proactive.

7 Reasons Why You're Struggling to Land a Data Science Job
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Here are a few simple steps that can help you make the difference:

Shortlist companies you’re interested in.
Check for relevant openings.
Reach out to the recruiter with your resume and portfolio explaining why you would be a good fit for the role.
Connect with other professionals. Get into the habit of networking even when you have a stable full-time job.

Joining data science communities online can also be super helpful!

And that’s a wrap. Here’s a quick review of what we’ve discussed:

Prepare for coding interviews. Practice on platforms like Leetcode and Hackerrank.
Tailor your profile to align with job requirements. But be consistent.
Put in efforts to work on your resume and project portfolio.
Start learning in public. Share what you build and learn.
Be proactive in networking with other professionals.

Good luck on your job search journey. I hope you land your data science role soon. What else would you add? Let us know in the comments.

Bala Priya C is a developer and technical writer from India. She likes working at the intersection of math, programming, data science, and content creation. Her areas of interest and expertise include DevOps, data science, and natural language processing. She enjoys reading, writing, coding, and coffee! Currently, she’s working on learning and sharing her knowledge with the developer community by authoring tutorials, how-to guides, opinion pieces, and more.