Data Science Career Track
Esme Gaisford
Before Springboard:
Freelance Science Writer
After Springboard:
Manager, Quantitative Analysis, FP&A at Pandora
"It took time and repetition, and Tammy was a great support in giving me resources, answering questions, and reminding me that the first steps always suck."
"It took time and repetition, and Tammy was a great support in giving me resources, answering questions, and reminding me that the first steps always suck."
Meet Esme Gaisford, a graduate of Springboard's Data Science Career Track.

Prior to studying data science at Springboard, Esme was a freelance science writer. When she co-founded a startup, she found herself working with data for the first time. "I liked the problem solving and the ability to analyze questions," she said.

While attending the Springboard Rise conference, Esme had the chance to network with a Springboard alum who was working at Pandora. After chatting for a few minutes, he offered her to refer her for the role she holds today. Now, Esme is a senior quantitative data analyst at Pandora.

What were you doing before Springboard?

Freelance science writer and did some data research/analysis at a startup I co-founded. I liked the problem solving and the ability to analyze questions I found doing data-science-y stuff at my startup.

What was it like learning data science starting from a non-technical background?

It was interesting. I had tried for a while, because I had been thinking off and on about doing something like data science, to teach myself code. I found that, for me at least, I need a little structure. I was not very good at just doing it from nothing, without any kind of project or clear application. Even if I had a long-term goal, if I didn’t have medium-term goal, it was hard to get there. So that was a big part of why I chose to do a program like Springboard, so that there would be this structure of goals around me, there’d be somebody I could talk to—the mentor part of the program.

It was challenging at first. The stats and the application of information and stuff I had more familiarity with. I took graduate-level statistics and biostatistics in grad school, and then, of course, I did a lot of calculus and statistical mathematics and logic reasoning in undergrad. But the pure coding part, it was like learning a new language. I’ve never done that from scratch, especially one that has a different alphabet. It’s just like pulling teeth.

I remember, some point, month and a half, two months in, suddenly things clicked, and every line of code I wrote was no longer a fight against myself to figure out what I was trying to say. I was starting to be able to think about what I actually needed the thing to do. If I don’t know the tool off the top of my head, the vocabulary or the language, I could look it up. But before that, it was just like every piece was a fight. And getting through that barrier was so hard. Once I did, it started to mean something, and the other parts of it that I already had, the other skills that I already had, I could start applying to this new way of talking about it and thinking about it.

What was your learning experience like?

Really good overall, sometimes frustrating (I came in with very limited coding experience and that slowed me down for a while), but the program taught me a lot, and my mentor was awesome.

What was the most challenging part?

The initial hurdle of learning a coding language was hard, and Springboard/mentor could have given me more support with that (my first mentor mostly, the second was much better; I was reassigned to a new one when the first dropped out). It took time and repetition, and Tammy was a great support in giving me resources, answering questions, and reminding me that the first steps always suck.

Prior to getting the job at Pandora, you joined us for the Springboard Rise conference in the fall. Would you mind telling us a little bit about your experience with Rise?

It was fun. A lot of the people there were current students, but definitely not everybody. I was impressed how many people came from far away. I mean, for me, it’s across the bay. I live in Oakland. But I met people who’d come from different parts of the country, both mentors and students, and a couple other alumni.

I remember a couple of the talks that were the very fast talks about different topics—how you could apply data science and data analytics was really interesting, and I really enjoyed that part. And then, of course, I ended up actually going to a talk that was an alumni talking about his current job [becoming vacant]. I mean, that ended up being a connection that … I went up to him, and I gave him my card, and I talked to him for a few minutes. He said, “Yeah, I’d be happy to refer you.” When he did, I applied immediately, had everything together. I got the interview, and then I ended up getting the job. I work with him now pretty much every day, which is pretty cool.

Wow, what a wonderful connection! How cool.

Yeah, I’ve always just thought that was crazy. I assumed when I saw him later he was going to be hounded by people because obviously everybody there, or a lot of people there, are looking for a job. But for whatever reason, he was not, and I went up to him, and said, “Hey, I’m interested.”

I think that also just speaks to your level of confidence. I think, a lot of the times, that can be intimidating for people, so I commend you on going up and doing that.

Yeah. I know it comes off as confidence. It’s more of just at some point in my life I was like, “I’m done letting my nervousness stop me from doing the thing.” Most of the time, if you go up and talk to somebody, most people aren’t going to shush you away. Even if they do, what have you lost?

Real Talk with Pandora Senior Quant Data Analyst

A Liberal Arts Major's Path to Data Science
What was the most difficult part of the interview for you, going into the job at Pandora?

I mean, for the whole process I think the hardest part for me was just continuing to do it, because I did take a while to find the right position. It was definitely keeping your spirits up and keeping your confidence up the whole way through. So I had to continue to practice and continue to keep my skills up while also trying to do other work. I did some freelance work on the side to continue to make money.

For Pandora, specifically, I think the coding challenge they gave me was definitely not necessarily easy, but in many ways, I feel like I was in the right place to do it because I had just had some very tricky ones for a few weeks before that with other positions that I did not do as well at, but gave me a chance to really practice some interesting ways of looking at things so that when I got the one at Pandora, I was ready.

So let’s talk a little bit more about your day-to-day. What is a day in the life for you, currently, at Pandora?

It obviously depends on the day, but largely is breaking down information of what are the ongoing projects that I’m doing or any new requests that are coming in from the team around me. I work on the analytics team that’s tied to one of the departments. In my instance, it’s the finance department. So theoretically, I could be helping anybody from finance gain information they need to do their work better. But that also means that I have ongoing projects producing dashboards or producing a greater understanding for the senior team, that I could be working on. So it’s very self-driven, it’s very open, depending on day-to-day.

What would you say is the biggest difference, for those of us who might not know that terminology, between a data analyst and a data scientist?

It really depends on who’s asking you, I think, because in the field, some career positions seem to use them interchangeably. I interviewed at one point with—I think it was Lyft or Uber, one of those. They literally were so tired of losing amazing data analysts because they go somewhere that gives them the title data scientist, that: “We’ve changed all the titles. Everyone’s a data scientist now.” I was like, “OK.”

The emphasis, in a more realistic way, is the depth of information you’re going to. A data analyst is going to look for larger, broader connections, I think, is a good way to put it, and a data scientist is going to be looking into the depth of theory. That is not always used that way, but that’s my understanding of the difference.

Do you have any tips for students who, similar to you, maybe don’t have that tech background, and they are just beginning our courses and are feeling a little bit overwhelmed?

For me, one thing I found actually really helpful, especially when I was having trouble with just the technical part, is my mentor and I set up a once-a-week check-in where I’d email her. I could, of course, email her any amount of time, and if she had time, she could respond to me. But it actually meant that I had a date that was halfway between [the weekly calls]. I think we talked on Tuesdays, so it was on Thursday evening or something, that I was forced to send her a little, like, “This is what I’ve been doing.” But it also, then, forced me on Thursday to break down where am I stuck, or where am I not stuck or whatever, so if I had a question, it gave me an outlet to talk to her, and I got a little bit further.

And that ended up actually being really, really useful, because just that little bit more time … And half the time, she’d be like, “It’s two lines of things that you’re just missing.” In my first capstone project, I had done this whole thing and maneuvered all this stuff around, and she’s like, “Yeah, so you’re basically trying to re-write natural language processing and make your own version of it. Here’s the tools that exist.” And I was like, “This is so much better!” But I had no idea. I didn’t know enough to know what to look for, but having that little bit more frequent check-in with her gave me that in feedback, just more often enough to make it easier.

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