Alumni Interview Series: Sally Son

Hi! It’s a new semester in unprecedented (*people need to stop using this word*) times but not all things need to be unprecedented! We are happy to resume our alumni interview series. Our alumni interview series showcases the most interesting QMSS alumni from a diverse set of fields and industries so this is a great opportunity to figure out what your interests might be after graduation.

First up in the 2020-2021 series is our amazing alumna Sally Son ’19!

Hi, Sally! Thanks for doing this interview. To start, what is your job title, company name and location?
Data Analyst 2. Stanford University School of Engineering. Palo Alto, California.

What do you do in your current role? What does that look like day-to-day?
In short, I do institutional research to help the school make data-driven decisions.
I analyze various kinds of data that the school has on topics such as student enrollment, finance, fundraising, faculty life, and human resources and help the school leverage data-driven insights in making its decisions.

On an ordinary work day, I might pull fresh data from the database, do some exploratory analyses using R, and find any unusual outliers or missing data. After cleaning the data, I’d plug the data to Tableau Desktop to create visualizations and publish dashboards on the school’s Tableau server. Finally, I’d share the updated dashboards with my team and the leadership along with some summaries of what’s new, concerning, or important.

What skills are most important in your current position and field?
I’d say the two very important skills in my current position and field are 1) attention to details and changes and 2) critical thinking skills that would help you decode the story that the data is telling. With attention to details and changes, you identify flaws, missing information, outliers, and odd observations in your data.
Then using critical thinking skills, you ask yourself why there are outliers and flaws in order to tackle challenges using your data-driven insights.

How did QMSS help you with your career?
QMSS prepared me with quantitative skills such as being able to use R and Python to analyze and model data.
More importantly, QMSS influenced how I work with data.
The three things that I keep in mind when I work with data are reproducibility, repeatability, and responsibility.

What is your favorite class at QMSS?
I liked all the classes I took at QMSS, and if I had to choose, I would choose Advanced Analytic Techniques and GIS & Spatial Analysis in Social Sciences.

What advice do you have for current students who want to get into your field?
To set yourself ahead of others in data analytics in higher education, I’d say look into creating data visualizations with Tableau Desktop, survey design, and program evaluation method.

This or that:
1. Beach or mountains?
2. Waffles or pancakes?
Hmm…this is tough because I love all forms of brunch. If I must choose, it’d be waffles.
3. Dogs or cats?
Ah, this is also a tough question because I’m allergic to cats and afraid of dogs.
But I will say I like looking at cute puppies from far away.
4. City or countryside?

Thank you again for the interview, Sally!

Alumni Interview Series: Kendall Loh

QASR is happy to announce a new blog feature, the Alumni Interview Series!

Considering the broad scope of what constitutes social science, and the myriad of ways that our skills can be applied, we will be interviewing alumni near and far in order to give prospective students some more insight into the Quantitative Methods in the Social Sciences program here at Columbia and post-graduation prospectives. We aim to provide particular attention to alumni who decided to pursue careers outside of New York and thus may not have as many opportunities to bring their valuable insights back to the community.

Our first interview is with Kendall Loh (class of ’19). Kendall is a software engineer at GNS Healthcare in Cambridge, MA, where he does testing on causal machine learning products. He also works with the data science delivery team to produce workflow documentation for pharmaceutical clients.

Could you describe your back ground?

I did my undergrad in biochemistry in upstate New York and worked in hospital outcomes research in Connecticut for a few years right out of college prior to applying to and attending QMSS. Coming in to the program, I had some math and general statistical analysis experience from my research work and undergrad, but not too much coding aside from little raspberry pi-type projects.

What lead you to apply for the QMSS program?

Really two things:
1) A coworker of mine left for QMSS a year before I did and she was having such a good time and learning so much that I couldn’t resist; and
2) We both worked with a lot of smart and hardworking statisticians who were able to work with a huge amount of data and draw insights quickly which was inspiring to watch in real time.
At the time, QMSS seemed like a great way to learn those applied coding and data science skills and make a little bit of a career pivot from biochem, which, I’m happy to report, turned out to be totally true.

Are there any skills, classes, or research opportunities in the program that you found very pertinent to your own goals?

I would say pretty much all of the classes were relevant to my goals. The coursework felt challenging yet accessible, and I learned tangible skills that are directly applicable to the job I have now. The core classes were excellent, Greg, Elena, Meghan and the rest were just awesome. I particularly loved Modern Data Structures and the Data Visualization classes taught by Dr. Brambor – totally worth one or two late nights coding and I use the data wrangling and viz skills every day at work!

Do you have any advice for individuals applying for the program?

We had a subscription to Data Camp which was useful coming in, there are others out there that also work. My work has a subscription to pluralsight, for example. I’d also recommend Coursera or other online resources to learn Python or R or brush up on statistics. Oh, and get to know your classmates, they will be your colleagues when you leave.
Generally, my advice to students and prospective students is just to build things and play around with data you find on Kaggle or wherever. Just like anything else, coding/data science/math skills are things that are ingrained with daily use – keep it light and fun and learn by doing. QMSS can/will give you a solid foundation to stand on, but then it’s your turn to build that foundation into something special.
Special thanks to Kendall for taking the time to speak with us!