Database Reference
In-Depth Information
So for these people, I describe my work as that we're figuring out what tools
will enable them to do that better and in a more systematic and efficient
way. So we build tools that empower those people, who are at the moment
reading a ton of stuff inefficiently, to proficiently consume more and better
information. Normally, if I explain that to anyone who's ever done any form of
consultancy, they end up with sparkly, delighted eyes, and they are blown away
by the fact that anyone's building tools to actually help them do this. And they
want access immediately.
Gutierrez:
When did you realize you wanted to work with data as a
career?
Heineike:
I think I realized it when I got into the economic consultancy
work, though I don't think that I really thought of it as being primarily about
the data. Rather, I think I thought of it as being primarily about understanding
systems through mathematical modeling. But it became very clear that the
data was a really fundamental component of that. The data limits how far you
can go, so I think that was when it really became apparent to me that I needed
to get into where the data was.
Gutierrez:
How did you get interested in math and programming?
Heineike:
Growing up, my family was extremely supportive and encouraging
of any enjoyment and interest I showed in mathematics. My eldest brother is
actually a programmer, and he was hacking around with computers ever since
he was very young and I was even younger. When I was pretty young, he tried
to teach me to code. I did bits here and there, and then I really got into it
when I started working. I think mathematics is a good foundation because it
teaches you to think very rigorously and logically. Programming became inter-
esting as soon as I had something I wanted to build with it.
For me, it's been very natural to go from abstract mathematics into data
analysis, and then further into programming. As I've learned to do better data
analysis and explore different areas of analytics, I've done more and more
programming. For a long time at Quid if we decided we wanted to try out
some new set of algorithms or approaches, I'd be the one to learn how to do
it and try it out, in collaboration with one or two others. I learned NLP when
we decided we should try analyzing large volumes of text. I learned large-scale
network analysis when we wanted to analyze our networks better. I spent
time learning how to structure code cleanly when I needed to bring my code
into production.
It's been an interesting progression of picking up skills that are really driven by
the necessity of being able to do what we want to be able to do and seeing
what kind stories that we want to be able to tell with the data.
