5 lessons from a data science intern at a tech unicorn

Photo credit: Shridhar Gupta

I’m currently an undergraduate student in data science. The last 12 weeks, I’ve been interning at Unity Technologies, a San Francisco startup that created a real-time 3D development platform.

How did I end up at Unity? I applied online, went through the interview process, and eventually accepted the offer to go to their HQ as one of 30 summer interns.

I thought I’d take this time to reflect and share a handful of actionable lessons, takeaways, advice, and thoughts from the experience.

My experience

I spent my summer working with the team at Unity Analytics. Analytics is a feature within the company’s suite of gaming solutions that is focused on helping developers deliver dynamic experiences.

I was also a member of the data science team where we extracted and enabled insights for customers and built tools to increase engagement.

Empathy is key

It’s a popular opinion that empathy is the most important skill for data scientists. I don’t know if this is true, but when it comes to skills like data analysis, results communication, and product intuition, empathy is key.

Even for something as simple as documentation, the ability to put yourself in the shoes of someone else can be an extremely powerful tool.

This is true for most roles, but I’ve found that it’s especially applicable for data scientists. Simply put, we work with data to provide value. When working with data on a daily basis, it’s easy to forget that it isn’t just floats, strings, bits, or bytes . Rather, it’s a representation of the users’ voice at scale.

Without empathy, we lose the bigger picture and fail to produce meaningful, impactful work.

Information isn’t enough

As data scientists, analyzing and providing insights is a big part of what we do. Notice that I said “insights” not “information.” This may seem trivial, but there is a subtle yet important difference.

Information is a collection of data points used to understand something, whereas insights use this information to drive action.

I thought about this distinction a lot over the summer. Early on, I found myself presenting analysis and often receiving questions like “So, what’s the takeaway here?”—a more polite way of saying “What am I supposed to do with this?” Upon reflection, I realized that my mistake was providing information, not insights.

It’s one thing to provide interesting analysis, but it’s a whole other thing to drive action. When you are finishing up your analysis, always ask yourself the following questions:

Have a beginner’s mindset

Make your data analysis trustworthy

Ruthlessly challenge assumptions