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Gutierrez: What in your career are you most proud of so far?
Heineike: The thing that amazes me the most is how far I, and the team at
Quid, have come. There was an early day at Quid when we created a visualiza-
tion poster where we used a network diagram to show a startup landscape.
When I first printed this thing out and showed it to everyone at an all-hands
meeting, after having collaborated with a few other members of the team, it
was obviously really cool, but it wasn't necessarily clear where this type of
work would lead. It's been amazing, partly because I've been fortunate and
partly because I've been on an amazing team, to take that initial idea and actu-
ally ask, “What would this look like if we built a real product around it?”
Zooming forward to now, it's amazing that we actually have an entire intel-
ligence platform built around this idea, where we have people buying access
to analyze the data. And we've got this paradigm that's really helping people
explore data at scale, which came out of some of those earlier ideas and early
analysis. That's kind of staggering, and I feel very excited and very privileged
to have been part of the work to bring our poster to life.
Gutierrez: What does a typical day at work look like?
Heineike: It really depends on what kinds of things I'm working on. I'm the
kind of data scientist who's thinking about how you piece together all of the
bits to make a product that works. This means that sometimes I work really
closely with the business side of our organization. So I'm generating analysis
for them and talking to them about how they're using it. And I'm trying to
empathize with the questions people are asking and trying to determine if we
are doing a good job of helping them answer their questions.
On other days, I'm working more on worrying about the nitty-gritty of what's
happening on the technical side and how we're actually implementing things.
Today, for example, I've been worrying about schemas and metadata. So I've
been downloading tons of documents and I'm running across them and check-
ing that all the metadata lines up in a way that I'd expect them to. I've also been
looking at our engineering tickets and QA'ing some of the data processing
and letting the engineers know if it works correctly. So my day-to-day really
depends on the kinds of projects I'm doing.
Gutierrez: How do you view and measure success at work?
Heineike: As an organization, it comes down to having delighted users—and
ever more of them. This obviously trickles back into what we're building. For
some data scientists, their goal is to optimize something within their system,
and so it's very clear that if they can make that number get better, they all win.
In our case, the tools and products we've built are for data exploration, so it's
a bit more abstract.
 
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