2020 Oct 31
This content is an excerpt from an interview I did with fellow SharpestMinds alum Amber Teng.
Q: How did you get into data science / machine learning?
A: I’ve had kind of an unusual path into data science, so I’ll start from the beginning and go into some detail to help illuminate what it took for me.
I grew up in a smallish agricultural town (Modesto, CA), where my dad worked at Safeway (still does!) and my mom was a stay-at-home mom. They really impressed upon me the importance of taking my education seriously, which was fine with me because I loved learning and I enjoyed making them proud of me.
Ever since I was little, I wanted to be a scientist. I loved tinkering and learning how things worked. My mom indulged my curiosity by taking me to the library (I would come home with a stack of like 12 books), having me help her in the kitchen (cooking = chemistry!), and getting me the occasional toy science kit.
That interest carried on through high school and into freshman year of college, where I had decided I wanted to study chemical engineering and become a flavor scientist, because chemistry was my favorite subject. I (adorably) thought that I would simply invent new flavors to make healthy food taste better, so people would have an easier time eating salads and vegetables, and thus be healthier overall. I hated eating salads and vegetables, so 17-year-old-me thought I was brilliant and that this was an amazing solution.
I kept up my education focus and work ethic throughout high school, and made it into Stanford for undergrad. Frankly, that was pretty unexpected for me — I thought I would be going to UC Davis and maaaybe Berkeley if I was really lucky. Around half of folks who graduate from my high school don’t end up going to college at all, so even these felt like pretty high ambitions. Of my graduating class of 500 that year, I think only around 5 of us made it into “top schools” (Berkeley, Stanford, Harvard).
What I was really not expecting when I went to Stanford was the culture shock I was in for. The vast majority of students at Stanford come from upper-income backgrounds, with a median family income of $167,500. They are, by and large, the kinds of kids who have college-educated, professional parents, go to the best, most well-funded high school in town, and have paid tutors to help them out in any area they’re struggling with. Meanwhile, I grew up with a HH income around a quarter of that, and the level of preparation I received in some areas relative to my peers was reflective of that difference. (My parents and teachers were wonderful and had done their best, but there’s only so much one can do with limited resources.)
Suddenly, I found myself feeling very insecure about my abilities (particularly my aptitude for math and computer science) and was really questioning whether I measured up to the other students. I didn’t realize that our backgrounds had been so different, since no one goes around talking about that sort of thing, so I attributed differences in performance to my own lack of ability. I was also the only one from my high school who went to Stanford that year, so I didn’t know anyone when I got there and had no one to talk to about what I was experiencing. The feeling of being an impostor never really went away during my time at Stanford, but I did at least get better at faking-it-’til-I-made-it.
I did make it through Stanford, although I ended up not pursuing chemical engineering and also needed to take a year off after junior year to help with my parents’ divorce (my mom is disabled and needed help selling our family home and moving out). I graduated with a B.A. in Psychology in 2017 — first in my immediate family to get a 4-year degree — but I felt like I had made a lot of mistakes along the way due to a lack of guidance and role models. Even just searching for my first job proved difficult, because I could really only turn to the career center for advice on how to navigate the job market for “educated professionals.” The pamphlets and 30-minute consultations they could offer couldn’t really fill in all the gaps, but after a lot of research and attending career fairs, I was able to land a job doing social media marketing for a small agency.
Without going into too much detail about one’s financial and overall career prospects as a psychology major with only a Bachelor’s, it became clear to me over the course of my time in that job that I wasn’t going to get where I wanted to go career-wise unless I made a big change. So near the end of 2017, I decided I wanted to go into data science, specifically focusing on machine learning, and threw myself into GRE studies so I could get my applications in in time for Fall 2018 admissions. (I’ll go into more detail about why I chose data science, and NLP in particular, in the next section.)
I enrolled in my M.S. as planned, doing my coursework and studying as much as I could outside class, focusing especially on stats, linear algebra, Python, and machine learning. The degree coursework was all in R, so I learned Python entirely on my own using a combination of online classes and a massive 1500+ page textbook (Learning Python). Toward the end of my first year (spring 2019), I landed a data science internship at Curology and worked there through fall. Then, at the beginning of my second year, I partnered up with an amazing mentor, Nina Lopatina, through SharpestMinds, because I had decided I wanted to focus specifically on getting a role doing NLP. At the end of the 10-week mentorship, I started looking for jobs, and got an offer to join Primer full-time in December of 2019.
I would need to defer the last semester of my MS program to start full-time, which was a tough call, but the experience was more important to me, so I did. It turns out that that decision was frighteningly well-timed, because the COVID-19 pandemic decimated the recent grad job market only a few months later. I have classmates who are still struggling to find jobs, and I easily could have ended up in the same situation. I realize that I am very privileged that my roll of the dice worked out so well.
All in all, it took about 2 years to transition from marketing into a full-time machine learning engineer role, from a background of relatively little math and programming experience. (Prior to 2017, I had only taken single-variable calculus, basic/intro statistics, and one Java programming class.) If I had started from a background of either more math knowledge or more programming experience, I think that could’ve been shortened to one year.