Erin Burnside went through Galvanize’s Data Science Immersive Program after becoming interested in playing with data while working as a flavor scientist (really). She now works at fast-rising productivity startup Asana in San Francisco, CA. This is her story.
What were you doing before you got into Data Science? What was the catalyst for going into this field?
I was working as a chemist at a flavor company near Chicago. Flavor companies are responsible for the “natural and artificial flavors” that you see as the last item on nearly any food ingredient list. Most of what I did was identify aromas that the brain associates strongly enough with sweetness and saltiness that they make you perceive sweet and salty tastes more strongly. It sounds a little deceptive when I describe it that way, but it was actually really helpful in allowing food companies to make their foods healthier by reducing the sugar or salt.
I loved being able to design and run experiments, but over time I noticed myself working really hard to minimize the time I spent in the lab in favor of playing around with the data I already had and making an interesting story around it. I knew this was an indication that there was probably a better career path for me, but I had no idea what it might be. Then we started this partnership that involved me working with a data scientist, and I think it took one conversation with him to know that I needed to be doing what he was doing.
How did you learn about the data science immersive?
I’m not a patient person, so once I decided that I wanted to transition into data science, I started getting pretty antsy. I spent a little time trying to teach myself and realized that at the rate I was going, it was going to be ages before I felt comfortable applying to data science positions. So I Googled “data science boot camp.” I’m pretty sure Zipfian Academy was the first result.
What’s been the most surprising thing for you about learning data science?
I came in totally ready to knock the math out of the park and really struggle with the programming. It didn’t even occur to me that I hadn’t done any real math in years, and it turned out my math was so rusty. It made the first couple of weeks a little disheartening. I’m happy to say my math muscles are back in shape now, though.
What was your favorite thing about learning data science in the immersive program?
Getting the opportunity to spend three months just focusing on learning surrounded by other (crazy talented) people who are also completely focused on learning. Once you’ve spent time in the workforce, going back to school feels like such a luxury. I think as an adult I had the perspective to really understand what a privilege that is in a way that I didn’t in college.
What’s something really cool about data science most people might not know about?
People talk a lot about how data science means something different to everyone, and a lot of times I see this brought up as a negative thing. I rarely see people discuss the positive side of this variability, which is that, at least as far as I can tell, a career in data science is endlessly interesting and customizable. When you’re interviewing, you can find positions that are looking for just about any combination of data scientist skills you can imagine. Then once you’re in a job, there’s so much room to shape the position to your own interests and style.
How did you end up working at Asana? What was the interview process like?
Funnily enough, I had actually first been introduced to Asana when someone recommended I use it to manage the process of deciding what jobs to apply to back when I knew I wanted a new job but had no idea what it should be. They were one of Zipfian Academy’s hiring partners, so I met the team at a hiring event on campus. The interview process was pretty typical – a take-home assignment, a phone call, some on-site interviews. Then I had to leave for a trip – months before I decided to attend Zipfian Academy, my fiance and I had already planned a trip for me to meet his extended family in Turkey in August – so I was dragging my computer around Istanbul doing video interviews from various family members’ houses. I found out Asana was probably going to extend an offer via a Skype call from his uncle’s living room, and it was really hard to remain composed until after we left later that night.
What’s your day-to-day like at Asana?
I get a pretty good balance of long-term projects that are driven more by the data science team and shorter term projects that tend to be more externally driven, and I usually try to do a little bit of each every day. In terms of the actual structure of my day, one of the things that has been a huge adjustment for me is the flexibility. In a lot of traditional office jobs, which is where I’m coming from, you’re expected to be in the office for exactly a certain number of hours every day, and once you get there, leaving isn’t really an option. That meant that if I was in a period of low focus, I mostly just ended up twiddling my thumbs and trying to look busy. Now when I’m at work and I catch myself twiddling, I have to remind myself that I can go take a walk for a minute. Or have a snack. The food here is pretty out of this world.
What’s been your favorite project so far since joining their team?
One of the big things over the past couple of months has been overhauling the company-wide metrics so that we have one core set of metrics that align with the goals of the company and are a common language for everyone. It’s been something that everyone on our team has contributed to, and it’s awesome to see such a big project go from start to finish and have a major hand in making it happen.
What advice do you have for an aspiring data scientist? Anything you would have done differently?
Two things. First, if you’re applying to positions, apply to everything you’re qualified for, including positions that don’t sound that interesting. Some places with really cool teams and positions need more than a job post blurb to be understood, so you’re really limiting yourself if you don’t give them a chance. Second, find a good balance of striving to learn new information and feeling confident in the information you already know. I have a tendency when I learn something really new and start to worry that it means I’ve been doing things the wrong way this whole time, and there are so many valid ways to approach problems that this is rarely true.
Ready to become a data scientist? Apply for Galvanize’s Data Science Immersive program now.