Database Reference
In-Depth Information
Gutierrez:
When did your appreciation change?
Smallwood:
I think it changed after making it through the early experience
of leading my first few teams. I made all of the standard rookie management
mistakes in learning how to manage, and I made hiring mistakes and had to
live with those. After having to figure out how to deal with all of those things,
you kind of learn.
Gutierrez:
What do you look for in people when in hiring?
Smallwood:
I would say the top things are hunger and insatiable curiosity.
You imagine a data set and you salivate at just thinking about that data set.
Those are the top qualities, because people who always want to dig more,
mine the data, and learn new things from the data are the people who are
happiest in this kind of job. Obviously, the technical skills are important. But
that's always the easiest thing to interview for because it's straightforward to
ask those technical questions.
However, it's not as straightforward to try to get a feel for how curious you
are. You can't ask someone, “How curious are you?” But you can tell by how
many questions they ask. And if you describe to them a data set and ask,
“What would you do with that data set?”, people either can't stop talking
about idea after idea, or they're like, “Oh, I don't know. Maybe I would look at
the average minutes”—or something inconsequential like that. So I obviously
look for the technical skills and the curiosity.
The last important facet I look for is tenacity. You're really never done with
data and algorithms, so tenacity is a core thing. Passion for our business and
what we're trying to achieve is important as well.
Gutierrez:
Do you look for tool-set knowledge when you're hiring, or is it
more of a matter of, “We need the thinking to be in place, and we can teach
you what you may be missing in your tool arsenal”?
Smallwood:
It's more the latter, though it's important that people know
some tools. If you've never worked with any kind of data querying, you're not
going to understand data that well, and you're going to have a long learning
curve to get to the point where you're efficient. So that would be a showstop-
per if somebody didn't know some tools at each layer of the data stack. But
the specific tool doesn't matter that much. Generally, once people know any
tool at some layer of the stack, they understand that layer and they're able to
pick up another tool quite easily.
Gutierrez:
What's the biggest thing you've changed your mind about with
respect to using data?
