Database Reference
In-Depth Information
Gutierrez:
Do you get enough mathematics in your day-to-day, and if so, do
you still enjoy it?
Smith:
So this is where I need change how I use my thinking time because
I feel like I do more latent math in my coding. I've actually been thinking about
this lately and came to the conclusion that I don't actually write down equa-
tions as much as I used to. At Bitly, I had people I could work through the math
with and we could work through a problem that way. They would sit down
and ask, “What does this equation look like?” They could figure out if what
we were trying to solve made sense from finding the math together. And so in
that case, collaboratively, you need that math to talk about the same thing.
Lately, what I've been trying to do is find more people that I can do that with,
because otherwise, if you're on your own, you're not going to sit down and do
a whole math exercise. I mean, I know people that do, but I'm not going to sit
down and do it by myself. It's nice to work with others. So I still enjoy it and
think I would enjoy it even more if I had more time to think about it and work
through some math with others.
Gutierrez:
What are your thoughts on hiring good data scientists?
Smith:
I haven't done much hiring myself. That said, I think it's the same rubric
as finding good information on the Internet. It's all about common sense, like
how did they approach a problem? What ways do they think about it? Do they
try to approach it from many different ways? Do they get stuck on something
and then give up? I think data science is a learnable skill, and it's not something
you necessarily need to come with. It's just something you need to develop.
Gutierrez:
What questions were you asked when you were being inter-
viewed at Bitly and here?
Smith:
For Bitly, because I was going in as an intern, it was more about what
have you done and can we see a sample of your code just to make sure you
actually know what you're doing? At Rent the Runway, it was much harder.
I had interviews with both the analytics team and with the engineers.
The engineers had more traditional tech questions like, “Here's a sentence.
Try to reverse it.” Or “Here's a binary tree, how do you traverse it, and how
do you make sure it's balanced?” For the data science interviews, it was cen-
tered much more around real-world case studies. They said, “Here's a prob-
lem we have. How would you approach it?” Then they asked questions like,
“What kind of algorithms would you throw at it? How would you calculate
the success metric? How do you know when you've won? What do you do if
you don't think you've won?” After that, we talked through the problems they
were facing here at Rent the Runway and how they've approached them. For
these case studies, I was then asked what I think would be ways to expand on
that and where their assumptions might be wrong.
