On International Women's Day, Serene Ow (left) and Tan Hooi Ling speak about the roles of women at Grab.
“I think when I interviewed, we were 10 people on the team at that time. In the past year, we've tripled."
Serene Ow, data science lead at Grab, sits in a conference room next to company co-founder Tan Hooi Ling. We've gathered here at Grab's new research and development center at Marina One for a number of reasons. The first is to give the curious a peek inside what drives the unicorn - which, thanks to Tan Hooi Ling and Anthony Tan got its start in Malaysia in 2012 - and the quickly-growing team which powers it. The second is to celebrate International Women's Day and some of the powerful Grabbers at the company's helm.
Hooi Ling says that the gender split at Grab across the board - women to men - is about 40-60, composed of over 40 nationalities. On Serene's team, the number of female data scientists makes up around 20-25 percent of the team.
“Anthony [Tan] and I have never looked at either of us as male/female, more tall or more skinny - what we are about is what we each bring to the table, and we've cascaded that in all our recruitment decisions," Hooi Ling explains. “It's about what you can do meritocratically, not who you are." So, at Grab, how does one measure merit? We've previously looked into what Grab seeks in its engineering candidates. What about its data scientists?
If you're worried that Grab might not take you if you've never worked in data science before - that's not as much of an issue. Hooi Ling, who reveals that she interviewed Serene, notes that Serene, an electrical engineer by trade, ended up in data science after trying other careers.
“There is no one-size-fits-all," she says. If anything, Grab welcomes different backgrounds - a combination of employees from fields including - for example - machine learning, optimization, programming, and statistics.
That said, the most important criteria is that the candidate has a good grasp of data science fundamentals, which are tested in a practical manner during the interview.
“We have different hiring managers, and some part of the interview requires you to think through a problem and see your thought process and how you solve it," Serene says.
Show the inside
During your interview, the solutions you present are important, but the
process of arriving at those conclusions are equally so.
“Different subteams have different focus areas, but they actually go through whiteboarding sessions, problem solving sessions - because what's really important is to see how each individual thinks through a problem because every single problem is unique," Hooi Ling reveals.
The best candidates get through such problems by combining good code with logic and creativity.
“A lot of what we're doing is probably going to become materials for others to learn from and copy in the future," says Hooi Ling.
In other words, if you want to work for Grab, you need to be comfortable with trailblazing in the heart of ambiguity. There isn't always a manual for what you're doing because sometimes, you're writing the manual. However, as long as you're clear in your thought process and can work with a team, you have a place with people who will help you feel your way through challenges.
“That's why I came along and joined Grab, I think, because I think...the scope of Grab's problems are very interesting because they are so complex," says Serene. She adds that that's also what makes her job unique; she likes that one can't find what she does just anywhere else.
Many startups will tell potential hires that culture fit is as vital to the job as a skillset. Grab is no different. Who does well on Grab's data science team? According to Serene:
The disparity between genders
continues to paint the tech industry as a man's world, and that gender gap widens when research zeroes in on tech roles within tech companies. For example, twice as many men work at Uber than their female counterparts; women at Uber are more likely to work at lower-level, non-tech positions like HR or marketing than on engineering teams.
To help retain women already working at the company, Hooi Ling shares about Women at Grab, the name given to the company's in-house program in which women mentor other women, professionally and personally. Started by a group of female Grab employees (or “Grabbers"), the program is open to anyone who is looking to grow within the company, and some of the women in the program have become part of a larger mentoring group at LinkedIn, through which they will mentor other women outside of Grab as well.
Grab also offers three days paid family leave, which can be taken at any time, no questions asked.
For now, Grab continues to look toward growing its tech team - data scientists included - and hopes that working retention and ongoing educational efforts into its culture will shape the company and the future of the region positively.
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