3 Ways to Secure Your Data Science Job From Layoffs in 2025

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The World Economic Forum expects 41% of companies worldwide to cut their workforce due to the rise of AI by 2030, while companies like Meta have announced plans for staff reductions this year.

This means one thing: even more tech layoffs are to come in 2025.

I personally know many colleagues who have been impacted by tech layoffs last year. This made me increasingly concerned about my own data science job, so I started doing some research. I spoke to senior and lead data scientists, along with software engineers and product managers to understand the impact of tech layoffs on data science.

I had 2 pressing questions:

1. How do I secure my data science job from tech layoffs?
2. Is it still worth becoming a data scientist in 2025?

Based on the information I gathered and my personal experience, I believe that data scientists will still exist in the next 5 years. However, it is only the “value-adding data scientists” who will stay, while those who don’t improve the company’s bottomline will be made redundant.

And while no job is 100% safe from layoffs, I will share with you 3 ways to become an irreplaceable data scientist.

By the end of this article, you will learn:

• How to get and keep a data science job that pays well
• How to layoff-proof your data science career and quickly climb to management positions

1. Build A Strong Foundation

 
As a data scientist, you must focus on building a strong foundation in statistics, machine learning, and mathematics. While tools and programming languages keep changing, core concepts stay the same. You see, AI models can help businesses make faster decisions with machine learning and coding.

However, a company will never solely rely on an AI model’s work to make decisions worth millions of dollars. They will need to hire data scientists — experts who can prompt AI, correct its mistakes, and deliver insights quickly. The data scientist will brainstorm the right techniques to use, shift gears when an approach isn’t working, and fact-check any output delivered by AI.

However, the company will require fewer people to do the job due to the efficiency gains brought by AI. These data scientists will be paid well, but they must have a strong grasp of core concepts related to statistics and machine-learning, along with strong logic and reasoning skills. While most companies today focus on implementation and speed, organizations will begin to favor data scientists with strong theoretical knowledge of machine learning models.

Here are some free resources I’d recommend for you to learn the underlying math and theory behind data science applications:

2. Choose Business-Facing Roles

 
Any employee who brings in direct revenue to the company is valuable. Unfortunately, many data science roles are focused on future impact rather than immediate revenue gains.

For example, I once worked on a 4-month project to segment our customer base for better targeting. At the end of the 4 months, the customer segmentation model we built didn’t make it to production because it didn’t work too well on real user data. We ended up ditching the entire project.

A lot of data science roles are like this — focused on experimentation. Data scientists often build things that might work in the future rather than projects that bring in money right now. Due to this, if there is a layoff and the company has to decide to let someone go, they will likely target the data science team that isn’t critical in driving direct business impact.

However, if you choose a data science position that is close to the business — one in which you directly work with stakeholders and sales teams to make revenue-driving decisions — then your job will be a lot safer. For example, if you work at Google and are able to advise the product team on which search feature will bring in more revenue to the company, your job has a direct revenue impact. This means that you’re more critical to the business and are less likely to get replaced.

3. Prioritize Visibility Over Everything Else

 
If you want to keep your job and get promoted, you must be seen. This is true for every role, not just data science.

Let me illustrate this with the example of two colleagues — Pam and Jim — both of whom are data scientists.

Jim is great at crunching numbers. He is a coding whiz who builds machine-learning models that are highly accurate and valuable to the business. But Jim never promotes his work. He usually stays quiet at meetings, and nobody uses his models because they don’t really understand what it does. When business teams need an analysis from Jim, they often find themselves staring at his spreadsheets, spending a lot of time trying to turn his numbers into a decision.

Pam, on the other hand, is decent at programming and number-crunching. But she spends hours promoting her models across different business functions. Any analysis Pam comes up with, she documents with a presentation or showcases in a dashboard, highlighting insights that are crucial for business teams to make a decision. She also actively voices her ideas during team meetings and explains technical concepts clearly to business stakeholders. As a result, Pam consistently gets better performance reviews than Jim. Most leadership teams know who she is and enjoy working with her. She gets promoted quicker, and therefore is less likely to be laid off when the company decides to cut costs.

The ability to communicate and promote your work is something all tech professionals must build to climb the corporate ladder quickly, and data scientists are no exception.

Key Takeaways

 
The job market is uncertain and it appears as though tech layoffs aren’t going away anytime soon. As a data scientist (or even an aspiring one), this can be overwhelming.

However, there still are ways to remain competitive in this job market and even thrive: by focusing on core concepts, working closely with revenue-driving teams, and promoting your work to stakeholders.
 
 

Natassha Selvaraj is a self-taught data scientist with a passion for writing. Natassha writes on everything data science-related, a true master of all data topics. You can connect with her on LinkedIn or check out her YouTube channel.

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