What do data science, data analytics, and business intelligence mean at Grab and how are they being used? - Wong Mun
The data science team takes care of the science. We build algorithms and models, and in general, translate research (existing and new) into applicable product features. So, from the moment a passenger opens the Grab app to the time a vehicle arrives, data science powers the thinking and decision-making on the most efficient routes, travel time, and price points.
Data analytics looks at data from multiple sources to find trends and patterns-these insights can be turned into business decisions. Business intelligence looks a lot at our internal operational data to find ways we can improve our business processes, operations, and decision-making.
What does it take to be a data engineer/scientist? - Fu Chen Chia
Data engineers need to take care of data warehousing, building pipelines, and ensuring availability. Other than being familiar with the current state of the art, they must also constantly be thinking about adopting newer big data technologies to help them continue to scale.
Data scientists, on the other hand, need to find problems in the business, ask questions about the problems, find data, build models/algorithms to solve the problems and validate the solutions. There are varying levels of science that you could use to derive different levels of quality in your solutions and results.
I am in my early 40s and just discovered ML in Apple WWDC last year. I was mind-blown. Any advice on how to break into the ML/DL career path? I'm a firmware engineer by training. - Terrence Goh
Career switches are always difficult. Fortunately, firmware and algorithms aren't too far apart. Consider building intelligent embedded systems? I think that would be a good and natural next step. And since you work on firmware, it also shouldn't be too difficult for you to understand how GPUs work or process data. Try to connect the dots, so you can move from one to the other.
How did you get into this career path? - Terrence Goh
I was fortunate to have training in science and engineering, then a stint in a startup that had to deal with lots of data, at a time when big data was getting more and more recognition. So, I was fortunate, and I'm grateful.
As a non-tech person, what are the minimum competencies required to work at a data science business unit? Where should I start learning? - Diego Terceros Arce
I would suggest focusing on fundamentals, such as statistics. It is a basic requirement for data science and you need to have confidence in how you understand the data and what it is trying to tell you. Find tools that can help you visualize data, especially if they are high dimensional. Then move on to modeling, and look at how well your models approximate the real system.
Do you feel that there is a talent shortage in data, especially data engineers, in Southeast Asia? How do you find the right people for the Grab data team? - Kai Xin Thia
I think there's a general shortage of data professionals, not just data engineers. At Grab, we face similar difficulties in finding good data engineers, data analysts, and data scientists. But here we think we should own the problem, so we co-train with academia to expand the talent pool. We think that is more sustainable and is a way of giving back.
What do you think about the future prospects of a data science career in Singapore, as our country is small? - Christopher Chua
Singapore may be small, but it is a highly digitized economy. We're also an international business hub, with many companies setting up shop here, and I do see companies hiring more and more data scientists. So I would say the future is bright!
In your team's day-to-day work, how do you guys do 1) data cleaning and 2) more generally data governance to ensure data security, and that data is used in a correct manner to predict/recommend? - Gary How
Data cleaning is done either automated or done manually. It is one of the most time-consuming tasks, but an absolutely necessary job. Unless data collection is perfectly controlled, the noise will always creep in.
Within Grab, we have strict policies on who can access and use data. In terms of using data in a "correct manner," I think this probably warrants a bigger discussion around privacy, ethics, etc. We generally consult widely within and with our customers before we embark on building such features.
Other than the pace, what do you think are the more significant differences between working in academia and working at Grab? - Brian Winata
It is mostly theory vs practice. Academia's mandate is primarily to educate and create knowledge, so their focus isn't on building products for the real world. At Grab, our mission is to identify the problems our customers face and to solve those problems. Data science comes into the picture where we apply our data skills to create models and algorithms that don't just solve problems but solve them well.
Was there a case where your team built a model that looked good initially but turned out to be unviable when deployed on a large scale? For such cases, is it better to explore alternatives or to invest more resources instead? - Brian Winata
Not just one or two, but in several early cases, we had difficulties trading off among conflicting design objectives, especially when we have to deploy in large scale. What works as a proof concept may not necessarily become a real product smoothly. Often times, we trade between optimality and computing time. Luckily for us, good old "divide and conquer" has been on our side many times.
What was the most challenging AI project that you worked on? - Benazir Abigail de la Rosa Muñoz
Most of the stuff my team works on is challenging! That's what makes it exciting. But I would say the most challenging project has always been understanding our customers. Human behavior is hard to learn, and we are still working on it today.
Which programming language do you mainly use in the team? - Thibo Gissel
Perhaps unsurprising, but we use mainly R and Python. But we also use write-in lower-level languages when we need to further optimize for performance.
Do you have any message for all the youngsters who are struggling in their careers? - Alvi Syahrin
Not everyone is lucky enough to find a perfect career from the start. Let's just say everyone has a purpose and role to play in this world. Have patience, keep looking, and never stop working hard!
The article was originally published here