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that our devs and our ops people don't quite understand yet? Why would I
risk it when I can use this other stuff that we already understand? So I'm very
conservative with the tools that are selected.
Gutierrez:
What do you look for when hiring people?
Foreman:
Obviously, if we know the technologies we're using and are going
to be using, we do want to hire people who are familiar with those technolo-
gies. We want to go after the folks that really get the technologies we already
use, are going to use in the future, or are in the realm of tools that we might
use in the future even if we don't know now.
However, I don't think that's the only thing you should look for. I look for a
couple of things that I think are very important, which is the ability to learn and
the ability to communicate. We want to understand that though maybe you
haven't used exactly what we're going to use in the future and maybe you've
used something else, that you can still learn these things. That's a tough thing to
grapple with and understand about a person, so you have to ask for examples.
You've got to hear them articulate problems they've encountered in the past.
And if they can't articulate situations in which they've encountered problems,
and how they used various approaches and showed resourcefulness by saying
something along the lines of, “I didn't know how to use it, but I grabbed it and
we used it this way,” if they can't articulate something like that, then that could
be a red flag. So we always make sure to look for that learning capability.
Part of being able to articulate a story like that is that you've got to be able to
communicate. I think it's essential for a data science team to hire people who
can really speak about the technical things they've done in a way that nontech-
nical people can understand. This is because as a data science team, you end
up working with a lot of very nontechnical teams. I work with the marketing
team just as much as I work with the dev team, so if I have a bunch of folks on
my team who can't communicate well, who just wait for work to be thrown to
them like some wild animal waiting for raw meat to be thrown over the fence
for them, then that is unsustainable and unproductive.
At certain organizations that have one key analytics project that just needs to
get better and better and better and better, it can make sense to hire people
who don't need to focus on communicating with others. For instance, maybe
it's ad placement in a giant search company, where you just need to hire people
who wrote their PhD thesis on that particular area, and you don't need them
to know how to communicate. You just need them to do what they do and
you'll get huge marginal benefits out of having them improve your AI models.
But at an organization like MailChimp, where gains are made through collabo-
ration and solving new problems, you've got to know how to communicate. So
that is a key piece I really go after. It's not just technological familiarity, but also
the ability to learn and the ability to communicate with other folks.
