DSI Graduate Works as a Machine-Learning Software Engineer for Yelp

Woojin Kim
Woojin Kim

Shortly after graduating from DSI in 2016, Woojin Kim landed a job as a Machine-Learning Software Engineer for Yelp, the San Francisco-based company whose website allows people to post and read reviews of local businesses.

Before enrolling in the DSI master’s program, Kim hadn't formally studied or majored in data science though he used it in his researh and studies.

As an undergraduate at Cornell, he studied chemical engineering. He later earned a master’s in the same subject from Columbia, after which he enrolled in DSI. While a student at DSI, he attended a tech talk hosted by Yelp, where he had an opportunity to meet a company recruiter – an opportunity he capitalized upon:

“I had the chance to talk with the recruiting coordinator afterwards about wanting to work for the company,” recalls Kim, “and it went from there!”

In this Q&A, Kim talks about his interests in data science and engineering, his job at Yelp and his life in San Francisco.  

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Can you talk about your job at Yelp?

I work on the Ad Delivery and Targeting team at Yelp. We create machine-learning models that predict which ads users might interact with when they visit Yelp. We look at factors such as the nature of the ad, the users' interaction with it in the past and the aspect of their visits. Ideally, ads will show the businesses that users are looking for. So a great ad experience would mean showing ads that fit well with the user's intent.

What other data techniques do you use in your work?

I use Bayesian statistics to improve decision making. And I implemented a gradient-boosted decision tree (GBDT) model and pipeline for prediction modeling.

Do you like the job?

It's a really great opportunity to be working on machine learning at this scale and impact, given Yelp's size as a company. It also helps that I was already a Yelp user and I get the opportunity to work on a product that I use.

Can you talk about your internship at IBM Watson? How did you get the internship and did you learn a lot from it?

I applied online for the internship with IBM Watson. I worked for a search engine startup within Watson, where I had the opportunity to apply machine learning techniques to cluster similar search results and news articles together, and to categorize different entitites into topics. It was my first experience working as a software engineer, so I learned a lot.

Did DSI help you get the internship or your job at Yelp?

It definitely helped. While I had some interest and background in coding, the program helped me learn the right skills. Having exposure to a variety of areas within data science also really helped me decide which area I wanted to pursue.

Was your time at DSI enjoyable?

Absolutely! I really enjoyed the program. Everything was so interesting, which made it fun to navigate all the different courses and offerings. I started off with a small interest in data science yet the master’s program helped me expand that interest and find a career I love. So I definitely got a lot out of the program. Another thing that really helped me was being introduced to other students in the program. I met fascinating students from a wide range of backgrounds who shared my interest in data science. They exposed me to interesting perspectives on everything, and it was a fantastic experience working with them throughout my time in the program.  

Do you use what you learned at DSI on your job?

From developing and improving machine-learning models to performing rigorous data analysis to following good algorithmic practices: Yes, everyday!

You studied chemical engineering at Cornell. How does that background help you now in your work? And why did you switch to data science? Are there similarities between chemical engineering and data science?

I already liked coding and had been working with data analysis for a long time, but hearing all the developments happening in the data science area in 2014 and 2015, I really wanted to be a part of it first hand. The switch ended up being a great fit for both my interests and skill set! At the end of the day, chemical engineering and data science are both quantitative-based fields requiring a lot of critical thinking. While I no longer get to think about heat exchangers on a regular basis, the general problem-approaching and solving skills have been invaluable experiences from my chemical-engineering days. 

You conducted biomedical engineering research for your other master's program at Columbia. One day might you merge biology and data science?

The intersection between biology, medicine and engineering used to be my primary interest. I first worked with cell-modeling research as an undergrad at Cornell, creating computational models of breast cancer and stem cells. Later, I worked with microfluidic devices to aid with imaging C. elegans worms. I feel like there is still a lot of technology from computer and data science spaces that could be applied to biomedical fields. It's a really fascinating area and I'm definitely open to working in that junction again in the future.

Do you enjoy working and living in San Francisco?

Life has been pretty exciting so far here in San Francisco! It's definitely a different experience from New York City, but a great one in its own way. It's really a unique opportunity being surrounded with tech developments happening all around. I definitely can't complain about the fantastic weather here either, especially for running outside. Also, I grew up in Vancouver, so it's reminiscent of home being back on the West Coast.

--By Robert Florida



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