The rush into data hiring

Posted by Q McCallum on 2022-11-09
shop with a 'we're hiring' sign in the window

(Photo by Eric Prouzet on Unsplash)

Today, Meta (Facebook) joins the growing list of big-name tech companies to cut jobs.

To those impacted by the recent layoffs: I wish you the best of luck on landing a new role.

**To the companies rushing to hire data scientists and ML engineers who have just been laid off: **maybe … slow down a bit?

Common complaints I’ve heard from data practitioners include:

  • “This company hired me too soon. They just weren’t ready.”
  • “They weren’t serious about data.”
  • “I could tell from the job posting that they didn’t have a plan. They were just lost.”

And they sometimes followed with: "… so that’s why I left."

You don’t want to be in that camp. Be prepared to show candidates that you’ve thought it through. That you’ve set them up for success. That the job you’re offering will actually be around for a while.

And to do that, you’ll want to get your house in order:

**1. Review and update your data road map / data strategy: **This should be more than a high-level “we want to do AI”; it’s best to list specific projects to undertake and the resources necessary to execute them. Doubly important if this will be your company’s first data hire.

2. Check on your data team: How are they doing? Do you really need more data scientists or MLEs right now? How will that help? What specific role are you looking to fill, and what skill sets do you need?

(Just because you have a project backlog, that doesn’t necessarily mean you need a larger team.)

**3. Evaluate your company’s data infrastructure, documentation, and so on: **If someone were to join in the next few weeks, could they hit the ground running? If not, what would stand in their way? Can you clear that path in time?

**4. Take an honest look at your company’s data literacy: **Do executives and product managers have a conceptual understanding what ML/AI is, and what it can really do? Before you say “only the data scientists need to know that,” remember that data success does not come from a silo. Bringing everyone up to speed on ML/AI concepts will reduce hand-waving and set more realistic project expectations.

Here’s the fun part: so few companies take these steps! You’ll really stand out to candidates if you do your homework.

Best of luck.

(Full disclosure: Yes, I provide services in this arena. No, this isn’t a solicitation. You can work with me, work with someone else, do it yourself … All I ask is that you take care of the points I mentioned above. You and your next data science/ML hires will benefit from you taking this seriously.)