What is your current profession/job? What did you study at Yale? When did you graduate?
I’m currently a Data Scientist at Facebook. I studied Statistics at Yale and graduated in 2014 with a Master’s degree in Statistics.
What do you like most about your current role? What do you find most challenging and/ or rewarding?
I’m constantly improving the current product and coming up with new ideas that will be used by billions of people. I hope my language skills could be better, not in professional scenarios per se, but in all kinds of small talk, which would make it easier for me to build connections with co-workers and understand them better.
How did your time at Yale shape your career trajectory?
There are theoretical classes that built my statistical foundation, whereas project-based classes gave me a lot of great hands-on experience. I also participated in the Association of Chinese Students and Scholars (ACSSY), took a Japanese class with freshmen, and joined different clubs where I met a lot of great people who constantly brought me great ideas.
Did you acquire any professional experience related to your line of work while in graduate school (either through part-time work, volunteering, networking, or other forms of training)?
Not really, except for one project-based class I took. To be fair, my academic program is very short (9 months), so there’s not really any summer/winter break for me to do part-time work or take on internships.
What advice would you offer international students who are interested in your line of work?
If you’re interested in hard-core machine learning, I’m probably not the right person to offer insight. But if you’re interested in working closely with cross-functional teams (product managers, engineers, marketing), using your statistical knowledge to find patterns in data, and translating that to actionable insights that make the product better, don’t limit yourself to the classical statistics books. Get more hands-on experience with data by participating in Kaggle, find data to prove your hypothesis on things that interest you in daily life, read statistical blogs, growth hacker blogs, product blogs, and public research reports by tech companies.