Gallant Pui has found his way into data science through quite a bit of a journey. With a background in engineering, he was inspired by his mentors to explore data science as a career.
Soon, he realised his background in engineering equips him with certain basics of data science such as maths and statistics and a fundamental understanding of domain knowledge. The only missing piece was his programming skills.
With his great interest in the data science industry, he dived into learning programming and other relevant skills and knowledge. When the right time came, he saw the job opening in Grab, and the rest was history.
In this session, Gallant shares more about his experience in transitioning from an engineering background to being a data analyst at Grab, and also more on his learnings and insights from working in Grab, one of the fastest growing companies in Malaysia.
Seeing how you transitioned from an engineering background into data science as a career, from a scale of 1 – 10, how difficult would you rate the process?
To be honest, it really comes down to how determined and how much effort you are willing to put into it. But in general, I think it would be safe to say it is a 6 or 7 out of 10. So it is definitely doable, especially if you have made up your mind to do it.
Before entering Grab, how did you learn the necessary domain knowledge to a company with such complex processes (eg: taxi hailing, food delivery etc)?
In my earlier stage of learning domain knowledge, I start off by trying to understand the business to my best capability, and this includes looking into the processes and functioning of similar companies such as Uber. I researched and learn how they solve their problems, to which I know would be similar to the problems faced by Grab.
So if you are looking to learn domain knowledge, a good place to start is to learn as much as you can about the company’s processes, and try to connect the dots so you can understand how it runs as a business.
What is the interview process of Grab like for a position in the data team?
I personally think Grab’s hiring process is pretty solid and systematic. The first round is very much focused on testing candidates on their technical skills, and this would include programming knowledge etc.
Then, the second round is focused on talent acquisition, where the hiring manager will discuss the expectations of the job position with the candidate, and evaluate if it is aligned or not.
Lastly, then third stage would include a phone call interview between the candidate and the team that they will be potentially joining, so the candidate can better understand the day to day tasks, what are the specific problems they will be working on, as well as what values can they bring to the team in general.
We understand you are a data analyst in Grab, but what are your specific roles and responsibilities in the company?
In short, my role is parked under the tech family of TISS (Trust, Identity, Safety and Security) in Grab. Within the department, I specialise in payments, where it revolves around the risks and identifying payment frauds amongst our customer base.
Nevertheless, as much as payments sound very transactional, through the payment processes, we get to track processes like customer onboarding, understand customers’ behaviours on our platforms etc.
With payments, there is also the B2B side of things, where we have to be the bridge between the customers and the merchants. This is why it is also important for us to build rapport and relationship with banks, so we can track frauds and protect our users better.
Just to understand better on the day to day tasks of someone with your role, what are the key responsibilities that a data analyst would hold?
On a day to day basis, you spend a lot of time looking at screens with numbers, charts and dashboards.
In the data science ecosystem, a data analyst’s main responsibility is bringing valuable and actionable insights to the table, so that it can be further repurpose. These insights can either be from a business perspective, where you can solve a pain point or contribute to a larger business objective; or it could be from a data science perspective, where these insights would be incorporated into the process of building predictive models by data scientists.
In a large corporation like Grab, is there a specific restriction as to which tools you or your team will need to use?
Our company works on a very flexible manner, where they do not set rules and restrictions that mandate you to use only selected tools to process and analyse the data.
The company encourages employees to be proactive, explore the necessary tools that can produce the desired results most efficiently. So we do have a lot of freedom to use various open sources out there, as long as we can perform our job well.
Catch Gallant’s presentation in D/M Summit: The Role Of A Risk Analyst At Work
With the scale of the company, I understand there is a large amount of data that you will need to handle. How do you do that?
This depends on a case to case basis, but most of our data are handled internally. But there are some instances, especially in regards to payments, we will need additional data from third party sources such as payment gateways, processors, banks etc.
With Grab being so data-driven, I am pretty sure there is a certain structure to the data science team. Are the functional roles of team members split according to different categories and verticals within the company, or is it based on a startup team structure?
Our working approach in Grab as a whole is very agile. We operate from a small team of specialised talents which consists of data and risk analysts, data engineers, data scientists and software engineers. Each will have a role to play, but we collaborate and work together closely to achieve the results we set out to do, be it to solve a business problem or to create a specific model for new products.
In your personal opinion, is data science THE industry to venture into?
Personally, I am really passionate about data science, so I would be bias and say, yes, 100%! Especially if you have a strong interest in it, then you should definitely take the leap and venture into it.
Other than the technical skills you mentioned earlier, what other skills you would recommend someone to have before they enter the data science industry?
Personally, I focused a lot on learning relevant soft skills before entering data science, such as communication skills.
Often times, people think techies don’t really need to put attention on communication skills, but I disagree. It is crucial for someone working in data science to be able to communicate well so they can fully understand the business problems they are trying to solve, gain a better overview and insight as to what is requested or expected by the stakeholders of the company.
Where do you think the future of the data science industry lies?
I think there are a lot of areas that the data science and analytics field can move towards. As of now, there is so much data that we have, but are just not fully utilising.
Many companies actually own so much valuable data that can translate into something meaningful for their business but it is still left sitting there because of the lack of resources. So with the advancement and incorporation of data science, the potential is limitless
What is your advice to anyone who is looking to venture into data science?
If I have to leave you with a pointer, it would be to always be curious, and ready to get your hands on to practice and practice. Only by actually doing it, you will be able to start somewhere.
With fast-growing companies like Grab relying so heavily on data, it says something about the importance and potential of the career path in the industry.
If you are interested to learn more about how data science is incorporated into various industries, do tune in to the rest of the session in the D/M summit now. All you need is to get the Lifetime-Access Pass to tune in to industry leaders share their valuable insights and experience.