A Q&A with Jingshu Liu, Senior Technical Director Data Science

Cornerstone AI is publishing a series of Q&As with team members to provide more information and context on their role at Cornerstone AI, as well as their professional background. This is the eleventh post of the series. Visit our blog to see previous posts in the series.

Your background is in economics and finance, and later, data science—can you tell me a little about why you chose to go into that field in the first place?

Economics and finance were the majors I picked when I went to college, and there was nothing really specific about that choice. I was so young, not really knowing what I was signing up for. Then during college, I started to pivot to a more technical aspect within the major. I took a couple of actuary tests, which involve a lot of probability and statistics. During my master’s program, I started looking for jobs in fields related to risk management. That landed me at American International Group (AIG). I was really lucky—that team was called “strategic risk management” back then, before data science was even a thing. That small team evolved into a data science team with hundreds of people, and I learned so much in that first job. I found out I really liked this field with its combination of technical problem solving and business strategic thinking, so I’ve just kept doing it.

You worked with companies including American International Group and Medidata before coming to Cornerstone AI—was there anything in particular that compelled you to make this change?

One big part is that I tend to follow the people I like to work with. When I moved from AIG to Medidata, there was a group of my previous colleagues, including one of my managers, who had also moved over to Medidata. I kind of followed the same path to Cornerstone, with Mike, PJ, Andrew, Richard, Yue—quite a few people on the team are from the previous Medidata group. 

Beyond the people, it's also that I enjoy an environment where I can sit down and do some focused analysis. That was the case for AIG in my first year, but not so much in the second year, so that had triggered me to seek out a change. And it was a similar story for Medidata—in the first four or five years of my time there, I did more hands-on work, then I transitioned into a managerial role, and that's a very different career path. I learned a lot in that role, but towards the end, I felt like it was a good time for me to come back to the technical field. 

And in general, the healthcare industry is something that's always in me. Both of my parents work in healthcare, so coming to this field makes things a little bit more meaningful to me.

On a day-to-day basis, what does your work look like? What drives you to continue the work each day?

It's slightly different day-to-day, and it depends on what project I'm working on. If it's an analysis project, I may spend time designing experiments, conducting analyses, and discussing results with the team. If it’s an implementation task, it’s more about writing code and tests, committing it to GitHub, pinging someone to review, discussing any issues, and then pushing the final version. So far, I have had a lot of time to sit down and do analytical work. That's the part I really enjoy.

What does the future hold for Cornerstone AI? 

My wish is that all the pharmaceutical companies and data providers start to realize how important data quality is to them, and then Cornerstone AI really becomes a cornerstone of their overall analytical data pipeline. Although many companies have their own data science team working on data quality control and cleaning, a data science team in a pharmaceutical company has so much on their plates already, and often they don't have the time or can’t afford the effort to solve this problem thoroughly. And to work on data quality one dataset at a time means lots of repetitions—time and effort that can be used on more creative tasks. I do believe we have a great solution to the very long-tailed data quality problem. We're going to keep working on it, and with a very focused effort. 

What are you most proud of in your professional and/or personal life?

Back at Medidata, I was able to see a product from the very early ideation stage all the way to a more mature stage, in which the machine learning algorithms my team worked on play an important role. I learned so much from working through that process, like how to work with different teams, what a product really consists of, and all the different aspects of the effort you need to put in to make that successful. I got to see not just the back-end development side, but also the marketing, sales, customer support, all of that. It was a great learning experience that I’m proud of. 

In terms of my personal life, I started backpacking during COVID, and I’m proud that my friend and I did the Tour du Mont Blanc last year. You can stay in a hut, but we booked too late, so we stayed in tents the whole time. It's a beautiful route, and the camping part made it very interesting—we had to figure out where to stay every day, sometimes at the top of the mountain because in Italy, you can only camp above 2,500 meters. Finding the right spot and then finding a water source and everything can be both challenging and fun. We took it pretty slowly so overall, it took us about 12 days to finish it, including a rest day in the middle of the trip. 

What are you passionate about outside of work?

Backpacking is certainly one of my passions. I wouldn’t call myself a big skier, but I am a ski enthusiast; I try to make a trip every year. Andrew was very jealous of my ski trip in March of this year. I also resumed playing table tennis recently, which is another big hobby of mine.