DSI Alumni Download: Q&A with Patrick Lewis, Data Scientist on Facebook’s Analytics Team

 DSI Alumni Download: Q&A with Patrick Lewis, Data Scientist on Facebook’s Analytics Team

Patrick Lewis recently completed the Data Science Institute (DSI) at Columbia University's master’s degree program and will join Facebook’s analytics team in the coming weeks. Here, he discusses his new professional opportunity, why he chose a career in data science, and how DSI taught him the skills he needed to land a job.

Congratulations on your new role with Facebook! How did you land the job? 

I was lucky enough to get a referral to Facebook through meeting a DSIer who was a year ahead of me and had gotten a job at Facebook. Through the referral, I got an initial interview, and went through three stages of interviewing. This culminated in my being flown out to the Menlo Park campus for a series of in-person interviews. Getting to know the people in DSI who are both in my year as well as a year ahead of me was a great resource and essential to my getting the Facebook job.

Based on your interviews, which data science skills do you think you'll use on your job at Facebook? 

The job involves a variety of technical skills, including writing Python scripts for analysis, running large SQL queries, and developing different machine-learning models. These hard skills are very important, but the role also requires one to think outside of the box when it comes to how to measure metrics and determine what a success is. This thought process is where the creative aspects of the role will really shine through, as the job often requires data scientists to come up with new ideas. Ultimately, it was the work I did for my Capstone Project, which was required of all DSI students, that helped me develop this creative thinking and get my job at Facebook.

Tell us more about your Capstone Project and how it spurred your creativity?

Patrick Lewis and capstone teamI worked on a team with four other students to develop a series of models to predict patient health outcomes after a major surgical procedure. We wanted to try to predict if patients would require additional medical care after the surgery, and what factors relate to whether the patient would need additional medical attention. We tried a series of different approaches and explored a range of additional datasets to come up with a fairly novel way to approach the problem. This opportunity to explore the creative thinking aspect of data science was a lot of fun, as well as being integral to my securing a job after graduation.

What do you like best about data science, and how did you get into it?

Midway through my undergraduate degree, I figured out that I wanted to pursue data science. I was working at an internship on a project to predict which donors affiliated with a nonprofit organization would eventually make a major donation. I found it meaningful to be able to make predictions that have a measurable impact on a company, and at a larger scale, on our society. The thing that I like most about data science is the range of effective ways that the data science skill set can be used. You can apply the skills learned in data science to nearly any industry or field, and have a positive impact on wherever you choose to apply your talents. Additionally, the creativity and innovation that is currently in data science is very exciting. Being able to participate in, and fully grasp, cutting-edge technological progress is something that many data scientists are good at, and this fits right in with what I want out of a career.

Which college did you attend and what did you study?

Before Columbia, I attended the University of Alberta in Canada, getting a bachelor’s degree of commerce while majoring in operations management. I was also a finalist for the Canadian Operational Research Society undergraduate paper of the year in 2017 and presented my work at the national conference. 

Why did you choose to come to DSI for your master's degree?

The program at DSI seemed the most comprehensive, with a variety of technical courses that really seemed like they covered the entire basis of data science. Additionally, the professors and instructors all seemed to be leaders in their fields, something that was confirmed during my time at DSI. My favorite course was Algorithms taught by Eleni Drinea. The pure amount of material learned in the class was great, and Eleni is an outstanding professor.

Do you have any advice for our prospective students?

Take all of the extracurricular opportunities that you can. Obviously, doing well in school is important, but be sure to take advantage of all of the professional and social opportunities put on by DSI and Columbia. In the end, one of the most valuable things you will take away from this degree is a network of ultra-intelligent peers, professors, and professionals. The overall highlight of DSI was all of the good friends that I made over the course of a year and a half. I will take that with me for the rest of my life.


Media Contact: Robert Florida | rsf8@columbia.edu | (201) 725-6435, mobile


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