Saturday , May 8 2021

How to become a data scientist, according to the head of data science of Grab

What does data science, data analysis and business intelligence mean in Grab and how are they used? – Wong Mun

The data science team takes care of science. We build algorithms and models and, in general, we translate (existing and new) research into applicable product characteristics. So, from the moment a passenger opens the Grab app when a vehicle arrives, data science fuels thinking and decision making on the most efficient routes, travel time and prices.

Data analysis examines data from multiple sources to find trends and models: this information can be transformed into business decisions. Business intelligence considers our internal operating data very much to find ways in which we can improve our business processes, operations and decision-making.

What does it take to be a data engineer / scientist? – It was Chen Chia

Data technicians must deal with data warehousing, pipeline construction and availability. In addition to being familiar with the current state of art, they must also constantly think about the adoption of new big data technologies to help them continue to climb.

Data scientists, on the other hand, need to find problems in the business, ask questions about problems, find data, build models / algorithms to solve problems and validate solutions. There are different levels of science that can be used to achieve different levels of quality in solutions and results.

I'm in my early 40s and I just discovered the ML in Apple WWDC last year. I was destroyed by the mind. Any advice on how to enter the ML / DL career path? I am a firmware engineer with training. – Terrence Goh

Career breakers are always difficult. Fortunately, the firmware and algorithms are not too far apart. Do you consider building intelligent embedded systems? I think it would be a good natural step forward. And since it works on the firmware, it should not be too difficult for you to understand how GPUs work or process data. Try connecting points, so you can move from one to another.

How did you get on this career path? – Terrence Goh

I was lucky to have a background in science and engineering, then a period in a startup that had to handle a lot of data, at a time when big data was getting more and more awards. So, I was lucky and I appreciate it.

As a non-technological person, what are the minimum skills needed to work in a data science business unit? Where should I start learning? – Diego Terceros Arce

I would suggest focusing on fundamentals, like statistics. It's a basic requirement for data science and you have to trust how you understand the data and what it's trying to tell you. Find tools that can help you visualize data, especially if they are large. Then move on to modeling and see how your models approach the real system.

Do you think there is a lack of talent in the data, especially computer engineers, in Southeast Asia? How do you find the right people for the Grab data team? – Kai Xin Thia

I think there is a general shortage of data professionals, not just data technicians. At Grab, we face similar difficulties in finding valid data engineers, data analysts and data scientists. But here we think we should have the problem, so we work with the academic world to expand the talent pool. We think it's more sustainable and it's a way of giving back.

What do you think of the future prospects of a data science career in Singapore, given that our country is small? – Christopher Chua

Singapore may be small, but it's a highly digitized economy. We are also an international business center, with many companies doing shopping here, and I see companies that hire more and more data scientists. So I would say that the future is bright!

In the daily work of your team, how do you do 1) data cleansing and 2) more generally data governance to ensure data security and that data is used correctly to predict / recommend? – Gary How

Data cleaning is performed automatically or manually. It is one of the most time-consuming tasks, but absolutely necessary work. Unless data collection is perfectly controlled, noise will always creep.

Within Grab, we have strict rules on who can access and use the data. In terms of using the data in the "correct way", I think this probably justifies a broader discussion on privacy, ethics, etc. We usually consult extensively within and with our customers before embarking on the construction of these features.

Apart from the rhythm, what are the most significant differences between working in the academic world and working in Grab? – Brian Winata

It is mainly theory vs practice. The mandate of Academia is mainly to educate and create knowledge, so their goal is not the construction of products for the real world. At Grab, our mission is to identify the problems our customers face and solve these problems. Data science enters the image in which we apply our data skills to create models and algorithms that not only solve problems but solve them well.

C & # 39; was it a case where your team created a model that looked initially good but turned out to be unprofitable when it was implemented on a large scale? For these cases, is it preferable to explore alternatives or invest more resources? – Brian Winata

Not just one or two, but in some early cases, we had difficulty untying between conflicting design goals, especially when we have to implement on a large scale. What works as a test concept can not necessarily become a real product without a hitch. Often, we exchange optimality and calculation time. Fortunately for us, the good old "divide et impera" has been on our side many times.

What was the most challenging IA project you worked on? – Benazir Abigail de la Rosa Muñoz

Most of the things my team is working on is challenging! This is what makes it exciting. But I would say that the most challenging project has always been to understand our customers. Human behavior is difficult to learn and we are still working on it today.

Which programming language do you use mainly in the team? – Thibo Gissel

Perhaps not surprising, but we mainly use R and Python. But we also use lower-level writing languages ​​when performance needs to be further optimized.

Do you have any message for all the young people who are struggling in their careers? – Alvi Syahrin

Not everyone is lucky enough to find a perfect career right from the start. Let's just say that everyone has a purpose and a role to play in this world. Be patient, keep watching and never stop working hard!

The article was originally published here

Source: HOB

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