Science and Technology Development Singapore

4 Singapore-based data scientists share how data has been impacting lives

Data science has been touted as the job of the future and a lucrative career, according to a Tech in Asia salary survey of data scientists.

But going deeper, there’s more to the job than just the salary. I spoke with four Singapore-based data scientists on their lives and what they think of the emerging field.

What made you pursue a career as a data scientist?

A mix of interests and skills

Since his undergraduate days pursuing psychology and statistics, Eugene Yan has always been interested in how people perceive, think, and behave. This required measuring perceptions, behavior outcomes, and making inferences from the data.

Along the way, he gained an interest and picked up machine learning, which, according to him, helps people learn from historical data and make predictions. Equipped with these skills, he started his career as a data scientist when he joined Lazada’s data science team in 2015.

Long-term love

Dr. Shonali Krishnaswamy was a data scientist as far as she could remember. “I started studying data science when it was called data mining,” she said. “I have always been interested in understanding data and the discipline of data mining, particularly the challenges of making data mining a reality in terms of mobile devices and mobile users.”

Dr. Krishnaswamy completed a PhD in distributed data mining at Monash University. Then, she joined A*STAR in Singapore, where she realized the real-world impact data science could create. Now on a newfound journey, she is the CTO and co-founder of AIDA Technologies.

A natural fit

“I kind of fell into it,” claimed Gene Yan Ooi, co-founder of Shentilium, a deep learning and machine learning consultancy.

According to him, the combination of his education, family, work experience, and personal inclinations made data science a “natural choice.” He also credited his father, a computer science professor, as one of the major factors.

Getting away from the lab

Dr. Yongli Hu, a researcher at the data analytics department of the Institute for Infocomm Research (I2R) at A*STAR, shared that her original background was in biomedical sciences. But her lack of dexterity for wet lab experimentation and her love to work freely steered her toward a career in data science.

What are myths of data science that need to be broken?

Using other terms interchangeably with data science

Ooi finds that the terms atificial intelligence, machine learning, and data science are often used interchangeably. “These three areas have overlaps, but are definitely not the same,” he shared. “While the definitions may vary depending on who you’re asking, I think most people in computer/data science would agree that they are not proper subsets of each other.”

Dr. Hu agrees and finds that the term artificial intelligence is overused and there is a need to recognize that not all data science activities contribute to AI efforts, even though the two are intricately linked.

It is more than just coding and algorithms

It’s wrong to believe that data science is just about code and algorithms. To Dr. Hu, communication is key. If not accurately and adequately communicated, the sophisticated solutions data scientists develop will be just a worthless piece of code.

Dr. Krishnaswamy argued that data science isn’t just “buying a data analytics platform, pushing your data through it, and pressing the button to generate a Linear Regression Model.” More is required.

According to her, “The key is to understand the business problem, understand what data is available, and map both to the right machine learning approach.”

More than just machine learning

Some believe that machine learning does 80 percent of the work. But in reality, it does only 20 percent. For Yan, 50 percent of his time is spent on data understanding, exploration, cleaning, preparation, and feature engineering. The remaining 30 percent is spent on execution, like building a POC, validation/AB testing, developing an API, deployment, and so on.

How it impacts our everyday lives

Yan thinks there are a lot of opportunities to use data to create positive impact and improve lives. According to him, data science alone has helped Lazada in many ways—understanding their customers, sellers, and products, automating and scaling their processes to reduce costs, and improving business outcomes.

For Dr. Krishnaswamy, data science is very powerful, as it can change the way we do things and allow us to see what is likely to happen in the future. According to her, data science can be applied in many ways like “predicting when machines will break down, enabling human decision makers to expedite processing of health claims, predicting which bank branches will potentially have a process failure in the future, and which employees are likely to leave an organization.”

Ooi shared that data science and data-driven approaches are an integral part of everyone’s lives. He also appreciates data science in action, which led Singapore’s GovTech to catch the rogue MRT train that was responsible for the spate of breakdowns using exploratory analysis.

Dr. Hu finds that data science has revolutionized the way people live their lives and made unpredictable futures predictable. She’s witnessed this in her projects, uncovering drivers of differential drug responses, predicting individual risks of contracting sexually transmitted diseases, and optimizing medical processes to reduce costs, for example.

Has being a data scientist changed your perception in life?

Ooi’s perception in life hasn’t changed much. But he does share that the job has changed his daily life, as it’s filled with data science-related activities. From waking to sleeping, he constantly checks on his models and adjusts them accordingly.

The impact to Yan’s life is on his personal mission. Now, he strives to use data to create positive impact and improve lives. His perception has also changed. “Sometimes, I can’t help but see things in our natural world and wonder how to measure them and use data to improve outcomes,” he said. “I’m also a bit more skeptical about claims people make and tend to be conservative and cautious when there’s no data backing the claim.”

Dr. Hu’s perceptions haven’t radically changed after being a data scientist. But she has learned how to better leverage data to make more informed decisions.

Advice for aspiring data scientists

In his blog, Yan shared the practical skills an aspiring data scientist should have:

  • Tools: SQL, Python and/or R, and Spark
  • Skills: Probability and statistics, machine learning, and communication
  • Practice: Projects, volunteering, speaking, and writing gigs

For him, starting new projects helps him learn a lot more beyond what is normally taught in schools or MOOCs.

Dr. Krishnaswamy, on the other hand, believes that a good data scientist must have a healthy curiosity for how the world works. “I get to see how banks work, insurance companies work, hospitals work, transportation industries work, etc. This is [all] very exciting.”

Ooi’s advice is to “build things.” According to him, almost all aspiring data scientists complete cookie-cutter projects. But what sets you apart and shows your genuine interest in pursuing the field is working on your own project.

KaggleUCI, and the Singapore government host a vast collection of open data sets which anybody can download and play with. Pick an area you have an interest in and get your hands dirty!”

This article is the fifth of the “Science and Technology Development Singapore” series, where the author delves into the development of science and technology in the country.

This article first appeared on Tech in Asia.