How to Make Yourself More Layoff-Proof as a Data Scientist | by Tessa Xie | Jan, 2024

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What tech layoffs taught me in 2023

Just when we thought that life is going back to normal after the pandemic outbreak, a wave of tech layoff caught all of us tech workers by surprise. In 2023, more than 240K tech workers were laid off across more than 1,000 companies; the most recent layoff at Google and Discord indicates that it’s continuing in 2024. Both my current company and my pervious company had more than one round of layoff during the last year, with some of my friends impacted.

Even though this is the not the first time layoffs happened in the industry, the cuts were deeper than in the past. Past layoffs had often focused on functions such as Sales and Recruiting that were immediately affected by slowdowns in the business and hiring. This time, however, the impact was across the board including technical functions like software engineers and data scientists.

It’s human nature wanting to distill patterns and learnings from things, especially as a data scientist (after all, that’s one of the key aspects of your job). So like a good data scientist, I sat down at the beginning of the year to conduct a “post mortem”.

While it’s hard to predict beforehand which team/who will be impacted by a layoff (unless you are one of the decision makers), I think as a data scientist, there are things you can do to make yourself more “layoff-proof”. And I will share a few here:

Stay up to date about the company and org’s strategic direction

Why? When it comes to layoffs, the decision about which teams/individuals to let go is usually highly dependent on the company’s strategy and prioritization at the moment. For example, if a company for several quarters has been trying to make dashboard building and metric reporting more self-serviceable, when a round of layoff comes, any team that focuses on dashboard making and metric reporting will be considered.

Remember, things don’t happen overnight; there are signs that you can take as a heads up. Priority shifts usually can’t be implemented overnight or even in one quarter. In the example above, before letting go of the whole team of DS who are building dashboards/reporting…

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