Meet John Walk: Ice Climber. Nuclear Physicist. Data Scientist.

May 14, 2019

Rachel Kirkwood

John stands out in a room. With a flaming red beard and an arm of tattoos, he looks like he might be more at home in a Nordic tavern than an office. But once you notice his Captain Hammer t-shirt, the Gandalf action figure on his desk, and start talking to him about Nuclear Physics, you see that he’s right at home in a room full of data scientists.

Science runs deep in John. So deep he spent a decade at MIT, earning a BS in math and physics, a Sc.D. in nuclear engineering, and staying on for a Postdoc at MIT’s Plasma Science and Fusion Center. “I shot things with lasers for a living, which you know is a recipe for job satisfaction,” he says. But the bureaucracy and lengthy construction process for these experiments began to wear on him. Looking for concrete problems he could buy into, he discovered data science, and jumped into Boston’s tech scene at a financial tech startup via the Insight Data Science fellowship. After a few years in the startup jungle, he made the switch to the Data Science team at Wayfair.

Wait, a decade at MIT, a fellowship, Boston startup, and now Wayfair? Yep, he’s been in Boston a good long while, but he wouldn’t have it any other way. He says that the vibrant and close-knit tech community is the best thing about Boston. “If you’re involved in the tech scene in Boston you either work at Wayfair or know someone who works at Wayfair.”

Plus, there’s the added benefit that the mountains of New Hampshire are just a car ride away. He started getting into rock and ice climbing in grad school and often drives up to New Hampshire on the weekends to climb. “Just don’t show my mom some of the more dangerous pictures.”

When he’s not scaling boulders or large precipices of ice, he works on the Merchandising team here, handling the unstructured data that Wayfair receives (text descriptions and product images) and transforming it into concrete structured knowledge about our products. “Essentially we’re trying to automate processes that humans can do quite easily, like reading the description of an item, which often means that it is very hard for a computer to do.”

I sat down with John to learn a little bit more about his career path and experiences at Wayfair. You can find his answers below!

How would you define data science?

“The joke answer is that we’re better at statistics than most programmers, and better at programming than most statisticians. But that’s not the entire equation, there’s always a third part of it which is domain expertise, which you often pick up on the job. For example, I worked at a financial tech start up and knew nothing about finance coming in…now I know a kind of uncomfortable amount about credit cards.

And that’s the job in a nutshell. Being able to synthesize domain specific knowledge with statistical rigor and computational strength. It’s the quintessential jack of all trades.”

 

What is your favorite part of being a data scientist?

“I love being able to open up problems that I began to address during my graduate research. I got kind of into the data science and machine learning side of research at that time, but in retrospect I was working with an extremely narrow subset. Now I find new implementations and applications for the same techniques in ways that I never would have even thought of before.

Case in point, I found that reading research papers in grad school used to be a bit of a slog. Now I read machine learning research papers in my free time, which is weird, but that’s how fast paced the field is moving. Something that is state-of-the-art soon becomes old hat and everyone uses it. Everything moves so quickly that it is interesting to stay on top of what is cutting edge.

That is a big part of why I left academia, the tempo of development is very different. In physics research you kind of lock yourself in a room for 18 months and come out with a perfect paper that 12 people will read (because plasma physics is not a large field). Whereas as a Data Scientist you can get something with real impact and pure results in a quick turnaround.”

 

What is your favorite part of working at Wayfair?

“The team. I interviewed with Wayfair for my first job out of grad school, but decided that I was young and it was the time to gamble on a small start-up. But I stayed in touch with people here and later on decided to make the switch.

I like that the team has an academic feel–many of the people on the team are from the STEM field and keep up with what’s going on through journal club etc. It’s a good learning environment. You look around the room and think, ‘Wow! Most of the people in here have PhDs! They’re all working on cutting edge research.’ And you can learn from them. For example, even though what I do has nothing to do with computer vision, I can go to a deep dive discussion and hear all about what’s happening in that world.”

 

What differences did you notice between working as a data scientist at a startup versus at a large company?

“I worked at a financial tech startup for 2 years, and at that company I was the first data scientist so I had to do it all. I met with stakeholders, wrote code, trained business analysts.That itself is a learning experience, you see all parts of the business. There’s a lot of low hanging fruit to tackle, so you get experience in a bit of everything.

Wayfair is a much bigger company, the size of the cohort of people joining Wayfair the same time as me was the size of the entire company I was leaving. That size presents a different set of challenges. The low hanging fruit is gone. We’ve already gone 80% of the way–two or three times by this point–and it takes some cutting edge stuff to move the needle on that 20%. So everyone is working on in-depth specific projects. You could become, the one-stop-shop for natural learning processes on your team, for example. That kind of depth in exciting in a different way.”

 

What do you view as your biggest accomplishment?

“Finishing my doctorate. Walking across the stage with that big puffy hat on felt so good.”

 

Submit a Comment

Note: Your email address is required to add a comment but will NOT be published.