An early career data professional at a media company, which I wrote about in my last blog, will eventually turn into a more experienced data professional — someone who’s been around the block a bit. And we want to keep these folks around because there is a lot of industry-specific context to our work.

So while bringing in fresh blood from outside our media industry is always great for the new vantage point we bring to our shores, we should want to cultivate our own people, too. 

As this professional matures, their needs and perspective on their career in media data also changes. Speaking with a senior data scientist at a preeminent German publisher just recently, they pointed out a number of areas with challenges for media companies looking to hold on to their data team members:

• Career banding being oriented at management for progression rather than allowing a longer individual contributor track.

• Communicating about the work the company is doing in data — both as a way to support team members thought leadership but also a way to connect with other folks working on similar problems

The career banding issue is actually one that Big Tech essentially solved several years ago — recognising exactly the challenge raised by our senior data scientist: “I want to stay on the technical side of things because it’s really what I enjoy and I think that’s where I’m good at so I’ve seen a lot of people moving away from actually writing code to managing projects.”

Meanwhile, “for many media companies, you don’t have something like technical career development. Most of the people go from like, junior developer, senior developer to just, well, management.”

In Big Tech, you will often find two parallel career tracks: One of the tracks heads into management, and another track is for individual contributors (IC) —  with principal engineer type of roles. Google has two roles in its IC track with lovely titles: “Distinguished engineer” and “Google Fellow,” which sound very elevated but do highlight the scholarly, rather than management, nature of the titles.

The whole design of the IC career track was to allow folks like our German data scientist to continue progressing in their career — taking on more critical engineering roles where their expertise would still be applied in the context of being a builder rather than a manager of people.

As data gets into a more and more specialised game, you do want to allow folks to continue to specialise and acquire extreme expertise. So it becomes more urgent to structure data careers to allow this in the first place, where progressing (making more money and having access to the most ambitious projects) isn’t requiring your collaborator be forced to move to management. 

Which doesn’t mean that mentoring younger team members doesn’t appeal. In fact, the drive to communicate about both expertise and work is another big motivator:  

“We’re getting younger people, and I’m really interested in helping them grow and also grow as a team,” but this mentorship doesn’t necessarily mean management. Skill transmission is as much training, brown-bag chats, and good team knowledge transfer as it is about having a boss who is nominally encouraging you to stretch yourself in your current role on your way to your next role.

Looking outward to the wider community of data folks — in media and outside of it — there is a question of how to build bridges. Also on the mind of our senior data scientist: “Before, there was Twitter and people used to be out there. Now it’s unclear where people are congregating,” making the need to outreach more formally — with blog, conference participation a more acute goal. (Shameless sidebar: It’s me, hi. If you want to speak at INMA events, please drop me a note.) Something where our data scientist would hope to see more encouragement from their company.

As it happens, this outreach can be a virtuous cycle — not just for data team members creating stronger networks and peers to draw knowledge from. It can also be excellent for recruitment. A few months ago, I casually mentioned a Medium post from a New York Times data scientist to someone in the data organisation at The New York Times. I said that I probably hadn’t managed to grasp all the finer points but found it super intriguing. Yes, this person said, and they had very much figured it would be intriguing for the kind of candidates they would hope to engage with — the post was out there in large parts to create excitement in the data community and hopefully get some applications for some of their open roles.

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