Iván Ugalde Gutiérrez studied actuarial  science at the Instituto Tecnológico Autónomo de México and worked for 10 years before enrolling in the master’s degree program through the Data Science Institute (DSI) at Columbia University. He worked as a researcher at Columbia’s Brown Institute for Media Innovation during his graduate studies and recently joined Amazon as a business intelligence engineer (BIE). Here, he discusses his trajectory and shares a few tips on how to ace behavioral and technical job interviews.

Congratulations! Tell us about your new job with Amazon.

BIEs are technical and analytical professionals focused on extracting value out of large and disparate data sets. They influence the direction of the business by leveraging the data to deliver insightful metrics that drive decisions. I will be based on Seattle, WA. However, due to COVID-19, Amazon has been really accommodating and allowed me to decide a date to move there when it feels safe to do so. I will be part of the logistics team in charge of driving performance measures and innovation within Amazon’s last-mile delivery network.

What helped to pique your interest in data science?

I came to New York without a job or school. While I waited for my work permit to arrive, I took some computer science courses and I loved them because they made a perfect fit and match with my mathematical and statistical background. After some research on what would be the next best course I could take, I decided that machine learning would be it. I took two online courses, and the next thing I knew I was studying for the GRE and preparing applications for the data science master’s programs at Columbia and NYU.

Why did you choose Columbia DSI?

I liked many of the faculty and, to be honest, having the brand “Columbia” on my resume helps a lot while looking for a job.

How did your undergraduate experience prepare you for the DSI curriculum?

For an actuarial scientist, it makes a lot of sense to jump into the data science field. I struggled a bit with algorithms and some computer science hardcore stuff, but I think that my undergrad took me half way there to become a data scientist.

What was your favorite course at DSI?

There is a tie between Applied Machine Learning taught by Andreas Muller and Applied Deep Learning taught by Josh Gordon. Those are courses where you learn 80 percent while doing the homework, but both professors spend a lot of time designing homework to be as helpful to students as possible. Those are long, time-consuming, and sometimes hard homework assignments, but at the same time are so interesting that if you plan ahead they won’t become a headache, but rather just a difficult hobby.

Do you have any advice for current DSI students on how to do well on behavioral and technical job interviews?

1. Prepare as much in advance for behaviorals as you would do for a technical interview. There is no such thing as too much practice for these interviews. Record yourself, ask people to be brutally honest about your answers, practice, practice, practice, and practice. You don’t have to have an answer for all the different questions they may ask you, but you need to have good and enough stories to answer any question. And practice enough to get to the point where it is almost mechanical to use the STAR methodology with any of your stories to answer ANY question.

2. Never, ever underestimate the importance of being proficient, or at least feeling comfortable, writing and debugging SQL queries. Most of us as data scientists spend a lot of time polishing our skills with Python/R, but don’t spend much time with SQL. I found these three resources extremely useful for practicing: HackerRank, Interview Cake, and stratascratch.com.

— Robert Florida