Metis graduate Yong Cho currently works as a Data Scientist at GrubHub, the food delivery company responsible for countless delicious meals delivered to my Brooklyn apartment. We caught up with Yong this week to ask about his role at GrubHub, his time at Metis, and his advice for current and incoming students.
Metis: Tell me about your background. How did you become interested in data science?
Yong: I've always been a numbers guy, as long as I remember, but it was really when sports analytics, and particularly NBA data, started becoming mainstream over the past couple years that I really found myself delving into the data head first during my free time and enjoying it more than my day-time profession (bond trader). At some point, I realized I'd love to get paid for the kind of data work I enjoy doing. I wanted to develop an in-demand skill set in an exciting up-and-coming field. That led me to data science and to me writing my first line of code, which happened last March.
Metis: Describe your current role. What do you like about it? What are some challenges?
Yong: As a Data Scientist on GrubHub's Finance Team, I'm applying my data visualization and data science skills in a wide range of projects, but all things that have an impact on driving business decisions. I love that I've been able to already learn of ton of new technical skills in just a short few months, and that my supervisors are constantly making sure I'm working on things I'm excited about, helping me grow from a career perspective. The fact that there are many more experienced data scientists here also has really helped me learn. Going off that note, something that was challenging at first was overcoming the initial awkwardness/imposter syndrome, feeling like I would ask the more experienced guys here what could potentially be perceived as dumb questions. I know there's no such thing, but it's still something that I think many people struggle with, and something that I think I've definitely gotten much better at while at GrubHub.
Metis: In your current role, what aspects of data science are you using regularly?
Yong: One of my favorite parts of this job is that I'm not restricted to one niche of data science. We focus on quick deliverables and break even long-term projects into smaller chunks, so I'm not stuck doing one aspect of data science for weeks or months on end. That being said, I'm doing a lot of predictive modeling (yay scikit-learn!) and quick ad-hoc analysis with SQL and pandas, in addition to learning about larger data science platforms and honing my skills in data visualization (AngularJS, Tableau, etc.).
Metis: Do you think the projects you did at Metis had a direct impact on your finding a job after graduation?
Yong: I definitely think so. Whenever talking to a data scientist or hiring company, the impression I got was that companies hiring for data scientists were really, more than anything, interested in what you can actually do. That means not only doing a good job on your Metis projects, but putting it out there, on your blog, on github, for everyone (cough, cough, potential employers) to see. I think spending a good amount of time on the presentation of your project material (my blog definitely helped me get many interviews) was just as important as any model accuracy score.
Metis: What would you say to a current Metis applicant? What should they be prepared for? What can they expect from the bootcamp and the overall experience?
Be pro-active: That means reaching out for informational interviews even before going to Metis, networking at various Meetups, and emailing former Metis grads for tips and resources. There are a lot of opportunities in data science, but also more and more people who are becoming qualified, so go the extra mile to stand out.
Ya gotta have grit: If you really want to get the most out of Metis, know that you'll have to put in late hours almost every night and live and breathe this stuff. Everybody at Metis is incredibly driven, so that's the norm, but if you want to excel and get a great job quickly post-Metis, be willing to be the one putting in the most hours and going that extra mile. Know that you have to pay your dues (most likely in the form of timeless hours on Stack Overflow), and don't relent at the first hurdle you come across, because there will be those on a daily basis, both at Metis and your data science job. A data scientist = a really good Googler.
Have fun: In the end, the reason we all joined Metis is because we love this stuff. Metis is probably the hardest I've worked over a 12-week span, but also genuinely the most educationally interesting 12-weeks I've had from a learning standpoint. If you're genuinely invested in your subject matter, as well as the skill-set you're learning, it'll show.