Emilie Guillou is going into her junior year at the University of North Carolina, Chapel Hill, studying statistics and analytics. Over the summer, she’s been working with Pinpoint’s data science team, learning both data science and software engineering tools and languages and helping build onboarding documents. We talked with her about why she joined Pinpoint, what she’s learned, and the gap between the academy and reality.

What drew you to Pinpoint?

Efficiency is one of my main interests—it’s why I decided to study statistics. I was really attracted to the idea of the intersection of statistics, modeling using data science, and business analytics that help companies make better decisions. I thought it would be a good place to apply some of what I’ve been learning in the classroom.

What have you learned about data science?

I’m not yet at a point in my studies where I’ve started creating models and running training data, but at Pinpoint I get to see how that process works. The team here is really deep into identifying signals, taking in data, running training algorithms, doing tests, seeing what’s helpful and what’s not. I’ve gotten a really cool window into how that happens.

For me, it’s just a challenge to learn the code well enough so that I can look at what the team is creating and understand the details. But I’m also sitting in on meetings with the CEO and CPO as they brainstorm about what signals should be included and how to take those ideas and translate them into a working machine learning model. Getting to see how the entire process works has been really valuable.

What are your top 3 takeaways from working at Pinpoint? (Remember, we’re recording this…)

First of all, everyone is always asking questions. I’ve noticed that at every level of the company, from the CEO to an intern like me, there’s a culture of asking questions and really trying to fully understand what the signals mean and not being afraid to ask questions and challenge assumptions.

Second, since I’m coming from a classroom setting, one of the things that has been striking is how much more complicated everything is versus what’s presented in a textbook! The beauty of data science is that the whole point is to work through the complexity and figure out how to constantly improve and work out the bugs, so it’s really a fun challenge with every project. But it’s eye opening to see how complex the reality is.

Lastly, I’ve learned quite a lot of technical skills, from understanding how to build a data science model, to using AWS, Docker, and Visual Studio Code. I’ve also gotten to see how Pinpoint manages testing of the product—it’s exceptionally detail-oriented.

In general, I’ve grown as a data scientist and appreciate being around the passionate team at Pinpoint. As I learn more about modeling and statistics, I’ll be able to contribute even more to the discussions, which is an exciting prospect since I plan to continue working with Pinpoint after the summer’s over.

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