Ever wonder what it’s like to work on work-related insights? For our very first interview of 2020, we catch up with Chua Pei Ying, a data scientist who’s also the APAC lead for LinkedIn’s Economic Graph team. She shares her career journey and gives her take on what’s required for aspiring data scientists to succeed in this exciting field.
Share a little about yourself. How did you come to be a data scientist, specifically, the APAC lead for LinkedIn’s Economic Graph team?
Becoming a data scientist actually came about by a series of unplanned but fortunate events. After six years as a neuroscience researcher with the DSO National Laboratories, I decided that it was time for a change. I interviewed at Grab for the role of a behavioural insights researcher. To my surprise, the final job offered was for a data science role. As it was still with the same team and sounded extremely interesting, I decided to take the plunge and go for it.
After several great years at Grab, I decided it was time for another adventure, this time at LinkedIn. I still handle data analysis but it’s on a more macroeconomic level. My team’s role is to harness the power of LinkedIn’s data to generate insights about the labour market and emerging trends on the Future of Work.
Looking back, I’d say that I’ve been extremely blessed so far. I am extremely thankful that I’ve encountered many supportive mentors and bosses in this exciting data science journey.
What’s a week at work like for you?
This tends to differ from week to week, but there are three core components.
The first is communication and stakeholder engagement. As a member of a horizontal team spanning several departments and geographies, I find it critical for the team to communicate regularly to maintain alignment and leverage synergies across the team.
The second part is conducting research and analysis. This includes a wide range of activities such as developing and testing new methodologies, generating insights from the data, and creating effective visuals to deliver the message effectively.
Last but not least is knowledge sharing. This takes many forms, such as getting involved in brown bag sessions, reviewing code, and maintaining proper documentation of the research methodologies.
What’s a common misconception about your work in data science that you would like to correct?
While technical ability is definitely necessary for becoming a data scientist, it is the soft skills that provide a differentiating factor.
Today, a lot of emphasis is placed on the technical training of data scientists, but the other aspects are really important too. The results that we generate can be very complex sometimes, and it is crucial to know how to communicate these findings to different audiences. Also, being able to listen to people’s concerns and understand the root causes, not just solving for symptoms of the problem, is key.
What were some of the biggest challenges you’ve faced in your career journey?
Every change is a challenge, and the first time is always the scariest. Looking back, I think that my first career change was definitely terrifying, especially after working six years in the same company. I think the challenge was overcoming my own fears and trying something else. What helped me was, for a lack of a better word, being a bit stubborn. I took a leap of faith with my career choices, and I’m immensely thankful that it’s been a great journey so far.
You’ve given talks at various platforms, including PyData and EdTech Asia Summit. Do you have any specific advice for women looking to break into public speaking?
I think it’s really about putting yourself out there. Join data meetups and interest groups. Participate in hackathons. These are opportunities to start presenting in front of an audience as well as meet people who share common interests. As you attend more of such events, I think the opportunities for public speaking will come.
Singapore is a very exciting place for data scientists as there’s a lot of data available on data.gov.sg. Just find something that interests you and do it! For example, one of my first presentations was at a data science meetup and it was about using URA and HDB datasets to analyse property prices. Sharing your findings on the different social media platforms is also important if you want to get more visibility and feedback on the work. If you do that often enough, the opportunities will come.
What’s your favourite passion project?
I have been working on a book called “One Minute Theorems”, which has just been published. This book covers theorems ranging across computer science, mathematics, philosophy, and psychology. The aim here is to strip away the jargon and distill the complex concepts into short, bite-size pieces that people can understand and relate to.
The inspiration for the book actually came from my wedding. My husband referenced the Gale-Shapley algorithm, which is used to solve for stable matching of couples. The basic idea is that the most stable matching occurs when all couples have no better alternatives. Many of our guests had no idea what my husband was saying!
With this book, hopefully, complicated concepts will become more accessible to a wider audience. I think sometimes the difficulty is in taking the first step to understand something, especially when that thing seems really complex. Hopefully, the book will help people realise that these theorems aren’t so scary after all!