Ever since we started doing webinars and workshops on Data Science, there is always at least one person in the crowd that asks us this, “How much does a data scientist in Malaysia earn?”

We understand that this is indeed a very practical question, especially for those who are looking to venture into data science as a career prospect.

As much as this sounds cliche, like all jobs, if you are only doing it for the money, you will burn out very soon. So before we actually tell you how much a data scientist earns, we will walk you through a little bit about what a data scientist does, as well as the career prospect of this industry.

What does it mean to be a Data Scientist?

There are many other positions related to the field of big data other than being a data scientist, which includes data analyst, data engineers, etc. So what does it mean to be a data scientist?

Think of a data scientists as more of a managerial role, in which you will need to steer your team to process the chunk of data collected, and turn it into meaningful insights that would contribute to the company’s growth.

So other than focusing on the execution and technical part of the job, it is important for you to have great organisation skills to see through projects, paired with good communication skills so you are able to lead the team, while act as an advisor to your stakeholders based on the data found.

If you are interested to know what does a data analyst or data engineer do, you can refer to this infographic below:

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Data scientist vs analyst
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What is the career prospect for a data scientist?

As we further dive into this data era, many people are considering venturing into data science as this seems to be a great prospect for career advancement.

The great thing about learning data science is that it doesn’t actually mean dropping whatever you have been learning or leaving the industry in which you have been working to go someplace else. Many people who are passionate about what they are doing, and want to gain more insights from available data they have on hand, can take up data science.

For example, you work in a hospitality organisation, and you want to further understand what makes your customers come back and what pushes them away, you can collect the necessary data and analyse them to solve your daily problems. Hence, learning data science in this situation would further help you develop a brand new skillset while increasing your value as an asset to the company.

But for the sake of discussing the career prospect of someone with a background in data science, there are two main types of data scientists, which is Type A and B. Type A data scientists (think “Analysis”), are the ones who take up the majority of data scientists. These folks are known to have vast knowledge in the industry they are in, and often do analytical work in order to gain insights that will benefit the organization.

On the other hand, Type B data scientists (think “Builders”) is the stereotypical programming geeks. These folks are incredibly well versed with various programming languages, and work on building tools and frameworks that can organize the data collected and turn it into actionable decisions.

For a layman’s understanding, the creepy pop-up ads like “Products you like” or “People you may know” are built by these Type B data scientists. For a lead data scientist, they usually are from this category and will have the ability to manage and lead type A data scientists due to their added knowledge.

Depending on which type of data scientist you are setting out to be, but often people start out with their data science careers doing more operational work like data mining, data analysis etc.

Later on, with a few extra years of experience, you can venture further into different verticals of data science (eg: manufacturing, research and development etc), and you may move from the more technical and analytical side of things (Type A)  to building models that allows you to interact with your users (Type B).

In terms of a more developmental progression, you can move up the corporate ladder by becoming a senior data scientist, and then later on the lead data scientist. The lead data scientist will have the opportunity to manage your data science team, as well as act as the advisor to the stakeholders based on your findings.

What is the average pay of data scientist in Malaysia?

After elaborating on data scientist as a career, we are here to finally answer your burning question.

According to recent job vacancies, an average entry-level data scientist or even data analyst is offered RM 4,000 – RM 6,000.

If you continue on to venture into this and become a chief data scientist, to which you will be spearheading and managing the team as compared to working on the technical part of things, your salary could potentially go up to RM 100,000.

Again, when you have more experience, paired with other skill set such as management skills, your salary would reflect based on what you have to offer to the company.

What are the key skill sets that would affect the salary a data scientist earn?

In terms of technical skills, one of the most obvious skills that will affect your salary is the programming language you know. According to the O’Reilly Data Science Survey which comprises of 600 respondents from different industries, the top two most in-demand programming languages are Apache Spark and Scala.

However, in general, data scientists who have mastery in as many programming tools as possible does have a significant impact on their salary. So if you are looking to further expand your career, a continuous learning process of various programming tools will definitely be beneficial.

Another very straightforward factor that affects salary is years of experience, specifically in holding data science roles, as compared to plainly data analysis work.

So as a rule of thumb, if you aspire to become a chief data scientist, always look for job opportunities that allow you to build more models as compared to just crunching numbers, as this can lead you in the right direction for further career development.

Through accumulated experience, you get to learn a larger skill set on top of technical skills such as good management skills to lead a team, paired with a good business intuition that steers them to solve problems. With these skills, it will help you contribute to your organisation more holistically which of course translates your value through the salary they are willing to offer you.

Job demand for data scientist and data professionals in Malaysia in 2021

Data science will still be the most highly in-demanded skill moving into 2021, it was even rank by #1 in the top emerging in-demand skill in Malaysia, and this growing trend has just get started – the reason being the shortage of data professionals in the country is still at an all-time high.

Hence, just scroll through the relevant job portals or platforms, you’ll find that many data-related roles are still vacant due to the local tech talent gap.

As at April 2021, there were more than 240,000 digital talents in Malaysia that had LinkedIn profiles; more than half of them were located in Selangor or Kuala Lumpur.

Data science skills are applicable across different sector and industry, in fact as we move into the Industry 4.0 and Big Data era, it is inevitable for companies to evolve and harness the data that they are currently generating or have acquired in the past. So, whether you’re in the finance, logistics, property sector, etc. learning data science skills will certainly accelerate your career growth.

What if you’re at your mid-career stage, should you learn data science?

If you’re currently a non/partial-technical professional or thinking to switch career into data science, there isn’t a greater time than this. This is because, through the accumulated years of experiences that you have, it’s probably the best moment as you already have acquired the domain expertise in your niche or market in which will tremendously help in your data science practice – having this means you know precisely which business questions or issue to solve using data science techniques and methodologies.

On the flip side, if you’re at an entry-level or early career stage, you might want to explore and determine what will your end-goal or ultimate purpose of learning data science is and the career path you’re embarking on.

You do not only need to have the technical skills like programming, statistics and mathematics knowledge but the ability to derive business insights from collected data – by truly understanding how a business operates in different markets and visualize it to tell a story from the data you’ve cleansed.

Data science is a life-long learning journey, especially as new and emerging technologies evolve, your career prospect will become extremely valuable and lucrative as you venture into different fields with your new-found knowledge and skill.

In short, whether you’re just starting out in your career, trying to switch into data science or becoming a data professional, learn the basics – the skillsets and industry knowledge to succeed that you can apply at work. Not to forget the open-source tools that are available which is part of a data scientist toolbox!

We hope this article has helped you gain a better understanding of the career prospect of becoming a data scientist. If you think data science is what you are passionate about, you can read more about how to break into data science with no technical background or join our Data Science 360 course to kickstart your journey in data science.

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