Diversity on your data team means supporting various styles of work, life

By Ariane Bernard


New York, Paris


Not too long ago, I had an interesting chat with a data scientist at a publishing company in Asia. The data scientist has a few years experience, which means they are absolutely the hottest commodity: still very much “doing the thing,” using fresh-from-school training, and not yet incredibly costly relative to the work performed. 

This person’s boss and broader team were thrilled with them. They received accolades and bigger assignments.

This person works at a prestigious media company.  This person loved their job. Really cared about the mission. But also: This person is now leaving, they told me.

Younger employees expect jobs to be flexible, and media companies would do well to follow their lead.
Younger employees expect jobs to be flexible, and media companies would do well to follow their lead.

What happened? They were hired in pandemic times, when work-from-home was an option, and this meant a life where this person could work on a job they appreciated but also cultivate an athletic pursuit that was significant enough that they participated competitively at a very high level. 

Doing this meant they had to remain in a particular location — and couldn’t return to “the office” when the publisher decided that work-from-home was no longer a thing as the pandemic abated.

Regretfully, our young data scientist decided they cared about a work-life balance where they could continue to nurture their athletic pursuit. In short order, a job offer from a top tech company materialised — of course allowing them to work from anywhere.

Obviously, I’m keeping the details on the publisher and location very vague, because the goal isn’t to name and shame. But really, when you hear this story, it makes you a bit mad. “Boo hoo, ‘tis so hard to find great talent.”

How much are we making it harder on ourselves really?

This isn’t strictly because data is a field that has a supply-demand problem between staff and recruiters. This problem exists all over tech and, in fact, even in non-tech jobs. At this point, a work culture that feels arbitrary and process-based just doesn’t sit well with your teams.

In data, this is worse because much of the work is naturally goal-based and has measurable outcomes. For example: We create systems that didn’t exist yesterday but exist three months later. They connect with one, two, three different other tools, but they used to not. Such-and-such team used to not be able to extract this or that information, but now they can. 

An employee's value should be measured by output instead of outdated process-driven methods (being in the office eight hours a day).
An employee's value should be measured by output instead of outdated process-driven methods (being in the office eight hours a day).

What I described here is a road map, and road maps by their nature have measurable milestones. If your data team doesn’t have milestones to achieve, there are deep structural issues with your overall company — and not just your data team.

But, on the other hand, if your data team is measured through process-driven methods (“Did I see you at work today, in this building we call the office?”) rather than through outcomes, do you really think they will respect the organisation they work for? Do you really think they’ll choose you or that they will stay?

This isn’t to say that process isn’t useful and at times necessary. But many jobs are far more meaningfully measured on outcomes in the first place. And if your company culture only justifies process-based accountability on the basis of it having been the dominant culture for decades, expect that your hiring problems will not be improving in the future.

This also speaks to who calls the shot for creating company culture in the first place. 

The technical side of media companies has often been ahead of the newsroom and commercial organisations in terms of remote work. Offshoring part of the business has been common for a long time in tech. When my engineering team at Taboola was based in Los Angeles building analytics products, and my boss was in Israel (and I was based in New York), I had years of experience of working with quality assurance teams based in Bangalore, Florida and Russia — at a traditional media organisation. 

You could dismiss this as the anecdote of one person, except you’d find this is a very common scenario for Millennial tech workers, and it has been so for years. 

What our Asian media company essentially told our data scientist is that they didn’t believe he could do a good job working remotely. Except, they had. They had already made the demonstration that they did do great work remotely. 

So there’s that double cost right there: A smaller hiring pond to fish. And, culturally, one that states, upfront, that satisfying a process-based evaluation of your contribution is going to be the preferred form through which you’ll be measured. 

Hiring (and retaining) would be an uphill climb indeed.

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About Ariane Bernard

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