Meet Jen Wang: Chemist. Traditional Chinese Medicine Enthusiast. Data Scientist.
May 14, 2019
How does a chemist with a postdoc in drug design end up working as a data scientist at an e-commerce home furnishings company? “Ha, so it’s a long story actually,” says Jen Wang, data science manager on the Marketing team at Wayfair.
Jen always had an interest in healthcare, and was well on her way to gaining a permanent position in the field; she earned a BS in Chemistry from Nanjing University, a PhD in Physical Chemistry from the University of Iowa, and worked as a postdoctoral research scientist in drug design at the Albert Einstein College of Medicine. But then she began to get antsy.
The more she worked on designing drugs as treatment, the more she became interested in promotional health, the prevention of disease before the treatment phase. “I started studying this on my own, and discovered how people are using machine learning with genomics and metabolomics to do very, very early diagnosis of disease, before bloodwork even shows signals.” Something about this approach “clicked” for Jen, reminding her of traditional Chinese medicine. “In China, the traditional Chinese doctors will look at your tongue and feel your pulse and they can tell you, ‘Oh you have an Yin-deficiency in your kidney (“肾阴虚”）.’” So Jen started a passion project to combine these two worlds. She began taking data science courses online at Coursera and wrote a proposal for a project that would take the early diagnosis, preventative care model of traditional Chinese medicine and go about it in a more scientific way by looking at metabolic signatures.
Once the project took form, she began to look for funding opportunities, but found them limited within academia. “So I started looking everywhere else, and got an offer from the Insight Health Data Science program in Boston.” As a fellow in this program she delved even deeper into data science and machine learning methods, and began to learn about different employers in the field. Read more below to find out how she decided on Wayfair and the course her career has taken since then!
Your path to data science is fascinating, but what led you to Wayfair?
“During the Insight Data Science Program you are introduced to many companies who are hiring–some related to health, some not. Wayfair was among them; I was very impressed by the team, and I liked the business challenges they discussed and the culture of the office. So eventually I joined Wayfair!
Was it difficult for you to move away from the health field?
“It was very, very hard to give up my work in the health field. At the time I joined the team here, I had a competing offer from a pharmaceutical company. It was a perfect fit for my old career trajectory, but one thing really intrigued me. I thought to myself, ‘If I choose that offer, I can see exactly what my life will be in 20 years.’ And that would be it. I wouldn’t have another opportunity to try something different.
But what Wayfair does was something completely new to me, that I knew nothing about! And even when I was in academia people always thought of me as ‘the geeky person who loves technology.’ So I felt like working at Wayfair was actually an opportunity to explore something that I’ve always been very interested in, but never had the chance to dig into.
Besides, I realized that data science and machine learning are core methods, and if I can get really good at those, I can apply them to so many problems. I could do side projects that weren’t just my day job…although I haven’t yet because I’m too happy doing what I’m doing at Wayfair!”
How did you feel about leaving academia?
“Not getting my project funded was the catalyst that made me consider alternatives to academia, but there was a reason that loomed larger even before that. The scientific problems I tried to solve were usually very fundamental–isolating malfunctioning proteins for example, studying their structures and dynamics, and designing small molecules that can fit inside pockets of the proteins that can modulate the function and therefore cure a disease. But this is only a small part of the drug design process and there are several downstream questions that cannot be answered by my research alone. So even if my work was successful it would still take 20 years for a possible drug candidate to be actually helping patients’ lives. That’s just the nature of academia. I guess as I get older I’d rather see faster turnover of the work I’m doing, to be able to see the impact more immediately.
In the 21st century the way we do research is changing. In the past, yes, academia was the best place to do research, but now there are startups, research units in industry, and all kinds of opportunities. Sometimes in industry this research can even be better, because there is better funding and collaboration. The reason I wanted to stay in academia was to solve problems, but now I can solve even more interesting problems outside of academia so why not?”
Besides the tempo of development, how different is your work experience here from the academic environment?
“When I joined Wayfair, a lot of my friends in academia said to me, ‘Oh, now you are entering a dark world…’ [laughs]. But I actually don’t feel that much difference. The research I’m doing is fun, it’s interesting–it’s actually more interesting now than what I was doing before, maybe because I had been doing that for 10 years and this is new. Also the mentorship I receive is comparable to what I was given by my Postdoc or PhD advisors. My manager, Anvesh, gives good mentorship but he also gives me space to really do things, the freedom to explore and do what I think is best.”
You started as a Senior Data Scientist, but are now a Data Science Manager. How have your day-to-day job responsibilities changed with that role?
“The major difference is that now I am responsible for other people’s development, not just my own. As a data scientist, if you make a predictive model that performs well in the business then you are in good shape. But as a manager, it’s more about overseeing projects, mentoring people on my team, and making sure that they are successful. For example, people who are PhDs taking their first job in industry, tend to overthink, to try to find the perfect solution the first time. I was the same way, so I have to keep providing timely and direct feedback to keep them on the right track. That kind of management is very important.
A big part of my day is also spent on discussing projects with business stakeholders, clarifying what they need and aligning the roadmaps with what they want to achieve. I spend less time coding, and more time mentoring, communicating, and making roadmaps.”
What do you like to do in your free time?
“I recently became a new mom, so my ‘free’ time has been full of… diapers [laughs] and a lot of joys, of course. Being a mom has been more challenging than anything I’ve done in life, but I fully enjoy it! It’s really amazing to watch a baby grow and learn something new every day.”