How much does a data scientist earn in Malaysia (2023)?

Jul 20, 2023

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 earn in Malaysia?

This is a fair question, especially if you are looking to venture into a data science career.

What is the average pay of data scientists in Malaysia?

A data scientist makes somewhere between RM 2,600 to RM 27,416 per month in Malaysia. The salary of a data scientist depends on:

  • Industry
  • Skills
  • Experience


Here are the different data scientist levels we have in Malaysia:

1. Junior Data Scientist

An entry-level or junior data scientist makes around RM 2,800–RM 4,694 per month in Malaysia.

The skills that a junior data scientist needs are SQL and basic Python programming.

Their job is to assist senior data scientists. For data preparation, they need to collect and clean the data. Making sure that the data is of good quality and ready for use in the projects.

After that, junior data scientists will assist in building machine learning models, assessing their performance, and using these models to answer business questions.

Lastly, they will need to prepare a report on the results and share it with the team.

2. Data Scientist

After working for 23 years, you can expect to have a salary of around RM 4,200 – RM 6,800 per month. The salary varies based on location, industry, company size, and technical expertise.

Data scientists in major cities like Kuala Lumpur tend to earn higher salaries.

Domain knowledge and programming skills in Python, R, and SQL also impact your salary levels.

A data scientist’s core job is to collect and prepare data for analysis. They perform statistical analysis to uncover insights, and use visualization to find trends and patterns.

Data scientists also develop machine learning models that can automate analytical tasks.

Finally, they communicate the key findings with stakeholders to drive business strategy.

3. Data Team Lead

A data team lead in Malaysia makes around RM 7,285 – RM 18,750 per month. Your salary varies based on the team size, responsibilities, and nature of your work (more leadership, more technical, or balanced).

The main role of a data team lead is to lead a team of data engineers, data analysts, machine learning engineers, and data scientists to solve problems using data.

You will need to manage and coordinate data projects end-to-end, from planning, requirement gathering, execution, and deployment.

You are required to decide the tech stack, create road maps, and set goals to continuously improve the data analytics capabilities.

Since data team lead is a leadership role, you will also need to hire and mentor team members, making sure their career development is aligned with the company’s growth.

4. Chief Data Scientist

The average salary of a chief data scientist ranges from RM 15,750 – RM 27,416.

Some companies (e.g., funded startups) pay  up to RM 100,000 per month if you can deliver values.

Sometimes the position might not be that of chief data scientist. You could be called the VP, Director of Data, or CIO. But, they are all similar. You will manage a data team and spearhead the company’s strategic direction together with other C-level senior management.

A chief data scientist’s work is less technical. When you have more experience, paired with other skill sets such as management skills, your salary will reflect what you have to offer the company.

Job demand for data scientists in Malaysia


Data science is still one of the most highly in-demand skills moving into 2023. It is ranked #1 among the top emerging in-demand skills in Malaysia.

This trend is still growing. The reason is that the shortage of data professionals in the country is still at an all-time high.

If you are not convinced, think about the growth of the volume of data we collect.

Scroll through any job portal, and you’ll find many vacant data roles. Due to the local tech talent gap.

Data science skills are applicable across different sectors and industries.

As we move into Industry Revolution 4.0 (IR4) and the 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 finance, logistics, the property sector, etc., learning data science skills will certainly accelerate your career growth.

What are the skills that affect the salary of a data scientist?

The most obvious skills that will affect your salary are:

  1. programming languages
  2. SQL, and
  3. BI tools

In general, data scientists who have mastered as many programming tools as possible have a significant impact on their salary.

So if you are looking to further expand your career, learning more programming framework and tools will be beneficial.

Another factor that affects salary is years of experience. Specifically in  data science projects, not just plain 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.

Don’t just work in projects that only crunch numbers.

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.

This includes management skills to lead a team, paired with a good business intuition that steers them to solve problems and grow the company.

With these skills, it will help you contribute to your organisation which
translates your value through the salary they are willing to offer you.

How can you study data science in Malaysia?

Depending on your goals and time flexibility, here are the three options you can consider when studying data science.


The internet is your university. With all the content on YouTube, Tiktok, and even Twitter, there is no excuse that you can’t learn data science on your own.

However, there are few challenges when we plan to self-learn data science. Many of our students who came to Data Science 360 actually tried self-learn before.

The no. 1 problem they face is the roadmap.

Yes, there is a lot of information available on data science, but it can be difficult to find the right resources for your learning style and needs.

It’s hard for you to tell which topics are up-to-date, which topics are relevant, and which are the best practices.

Many people also said that self learn requires a certain level of self-discipline.

Given we have so many things like work, family, and friends, finding time to prioritize learning is pretty challenging.

Another problem with self-learn is the support.

When you learn by yourself, it can be difficult to get feedback on your work. It is also hard to identify the areas where you need to improve, since there is no coach or trainer for you to ask questions when you are stuck in the assignments. 

If you need support on your self-learn journey, you may join some of the popular communities like Facebook groupData Science Stack Exchange , or Kaggle forum. There are thousands of members there from all around the world ready to help you.

Get a degree

Many people believe that data scientists must have a Master’s or Ph.D. degree, but that is not true.

You need a Master’s or Ph.D. degree if you are working in a specialised field, or the position requires you to conduct research from ground up.

Most companies don’t even require a data science degree for a data scientist position.  They are willing to hire you as long as you have a degree in a related field such as computer science, IT, software engineering, or mathematics.

However, do take note that large corporates and MNCs often require a minimum of a Bachelor’s degree.

So, if you can afford the fee and have 2 years to invest (for a Master’s), studying for a degree is a formal way to receive data science education.

Make sure that your degree covers the fundamental data science topics such as statistics, machine learning, and visualization. Or you can watch this video and learn how to pick the right university if you are looking to study in Malaysia.


Bootcamps are a good option to learn data science. Especially if you don’t wish to spend 2 – 4 years to obtain a degree.

The key to success is the time commitment. If you are working professional, there are part-time bootcamps can take it and set aside 1 hour to learn and practice data science for 30 days.

You should always pick a bootcamp that focuses on hands-on experience and results driven. It should cover data science skills like Python, SQL, data visualization, machine learning, and statistics.

Also, look out for robust support through live events, resume help, and not just video tutorials. Make sure that the instructors and assistants are experienced in teaching. Ideally, they should be a practitioner too. 

Having an instructor from the industry can make sure that the skills you learn are always relevant and is needed by the workforce.


If you are a professional working in another fields like business, computer science, social sciences, or healthcare. But, you want to boost your data analytics capabilities, a data science certificate is what you need.

A certification quickly validates core data science skills. It complements your current job, strengthen your resume, and fast track your progress to a data career.

Anyone wanting to quickly gain a solid foundation in data science before pursuing higher education like a data science’s degree. Certificates establish core knowledge to build upon.

If you want to understand what topics should a proper data science certification cover, you may check out Data Science 360 program and use the topics as a reference.  

Should you work for startups or corporates?

If you are looking to make real impact in the world working on innovative products and challenging problems, startup is a good option.

They always move at a fast pace and need to find experienced employees to fill up the roles and get things moving quickly.

Startups usually pay more for a data scientist, especially the funded ones.

In the case where they offer a lower salary, they would compensate you with other benefits such as equity or the opportunity to work on cutting-edge technologies.

Startups typically offer more opportunities for career growth than corporates. This is because startups are growing rapidly and need to hire new talent quickly.

As a result, you may have the opportunity to take on more responsibility and learn new skills in a startup environment.

If you are experienced or have other skill sets that complement your data science skills, you can always negotiate a good deal.

If you are in your mid career, and looking for a more relaxed work-life balance, then corporate environment might be a better option. 

A corporate job is less liberal compared to a startup, and your job scope is well defined. But you can learn systems and structures. You also get to work in an environment with a different culture.

Working in a corporate allows you to juggle your time effectively, and since you always have colleagues to back you up, it’s easier for you to take leave and handle personal and family matters. 

Type A vs. Type B data scientist

There are two main types of data scientists: Type A and Type B.

Type A data scientists (think “Analytical”) are what most data scientists are.

They have vast knowledge of their industry and often do analytical work in order to gain insights that benefit the organization.

On the other hand, Type B data scientists (think “Builders”) are the typical programming geeks.

They are well-versed in programming languages and work on building tools and frameworks that can integrate data into working applications.

Type B data scientists are responsible for creating creepy pop-up ads like “Products you like” and “People you may know.

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

This is not stereotyping and and you can always move from one to another. Often people transition from a technical and analytical side of things (Type A)  to building models that allow you to interact with your users (Type B).

Should you still learn data science even if you’re in your mid-career?

If you’re a non-technical professional but thinking of switching careers to data science, there is no better time than now.

Through the years of experience that you have accumulated, it’s the best moment now, as you already have domain knowledge in your niche.

This will tremendously help your data science practise.

You know what business problems people are facing. The burning issues that need to be solved.

You know what data is needed, and you just need to use the right data science techniques and methods.

You do not need to have strong technical skills like programming, statistics, or mathematics.

But the ability to derive business insights from collected data—by truly understanding how a business operates in different markets and visualising it to tell a story from the data you’ve collected.

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 include data analysts and data engineers.

So what does it mean to be a data scientist?

Think of a data scientist as having more of a managerial role.

You need to steer your team to process the data you collected and turn it into meaningful insights that will contribute to the company’s growth.

Other than focusing on the execution and technical parts of the job, it is important for you to have great organisation skills to see through projects, paired with good communication skills.

So that you are able to lead the team while acting as an advisor to your stakeholders based on the data found.

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

Also, check these videos out:
Data scientist vs. analyst
Data scientist vs. data engineer

Do you have to learn everything all over again?

The great thing about learning data science is that it doesn’t require you to drop whatever you have learned.

You can stay in the industry in which you have been working, and become more valuable.

Many people are passionate about what they are doing.

They want more.

They want to gain more insights from the data they have on hand but don’t know what to do. That’s when they need data science.

For example, if you work in a hotel and want to understand what makes your customers come back. You can collect data and analyse customers’ behaviour to answer business questions like:

  • What are our peak booking periods?
  • How can we optimise pricing and staffing for those times?
  • Which guests are most likely to be repeat customers?
  • How do ratings on review sites correlate to future bookings?

Applying data science in this situation will help you further develop a new skill while increasing your value as an asset to the company.


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

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 data science journey.

What other data science questions would you like us to answer?

Let us know in the comments section below. Like us on Facebook and subscribe to our Youtube channel for more content like this.

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  1. PL Lee

    Your article should be dated. Information for the field is time sensitive. If the information give here is 3 years old. It may not be valid anymore.

    • Reuben Chng

      Good point. Check out the date above. 🙂

  2. Saw Leng

    Hi, may I know which country and universities are famous for this course?

    • Dr. Lau

      It depends on the focus of the data science degree you are looking for. Some data science programs focus more on the comp science part, and some focus more on the analytics and maths part. I find that most of the good programs are offered in UK and US, for example, MIT, CMU and LSE offered Master programs with a good balance in technical and theory.

  3. Mei

    I’m from a healthcare background. Did not really find something to specialise in so my job is mostly quality and operations. Would it be wise for me to go into this field to be employed? Also am I considered a fresh grad and getting a fresh grad salary despite my past experience?

    • Dr. Lau

      Hi Mei, certainly. Data Science is about using code to build mathematics and statistical models in a scalable fashion, and apply it to industry problems. In fact, domain knowledge is the key and your unfair advantage, especially healthcare is such a hot field right now.

      Another misconception is the fresh grad part. No, you should never consider yourself a fresh grad. Your past experience is useful, you are an experienced team member with new skills, how do you think that sounds?

  4. Baoee

    How do I choose between math based and comp sc based data science degree?

    • Dr. Lau

      Hi Baoee,

      Maths based data science degrees focus more on the fundamentals, statistical modelling, calculus, and etc. Whereas comp science based ds degrees focus more on the algorithms, and other computer subjects e.g. networking, operating systems, and computer architectures. Which one are you interested in?

  5. Lee

    Any idea on how is the masters of data science and analytics offered by USM and masters of data science offered by UM? Thanks in advance.

    • Dr. Lau

      What I like about both degrees are:
      – Programming for data science
      – Machine Learning for Data Science

      What I like about USM:
      – Multimodal IR
      – Data visualization and visual analytics
      – Text and speech analytics

      What I like about UM
      – Research methodology
      – Data mining

      Both universities are teaching Big Data, which is good to have but not so applicable in the industry and research field. On the technical side, Hadoop is a good technology, but the adoption rate in Malaysia is super low. Which means you have a slim chance of getting a job afterwards.

      Both universities also do not cover much on data engineering and even the foundation of data science. it is due to the design of the course and they can’t cover that many topics.

      Most data science graduates focus too much on the data analytics part and neglect other important aspects. You should look into these areas for example unstructured data, text mining (includes topic detection, sentiment analysis), and multimodal IR (how to combine text, audio and video)

      Other factors you might want to consider while selecting between these two universities are their industry connection and facilities. Most data science services are cloud-based today, except for deep learning that requires GPU. A university that provides better facility will also be a plus point.

      • Fadhil

        Hi Dr Lau,

        That’s a good insight. As costs is my biggest constraint, I can only choose local uni to do Masters in Data Science. Which university would you recommend?

        • Dr. Lau

          For local universities, I quite like UM and UTM for their course structure, alumni network, and also their facilities. Maybe you can share with me what is your career goal such as the role and industry you want to be in, and also some of your current findings for local data science programs? Then I can point you in the right direction.

  6. Nicholas Tee

    Hi Dr.Lau,

    What do you think about studying abroad for Data Science related course, such as studying in Singapore? Is the degree more valuable as compared to studying locally in Malaysia? Would be grateful if you could provide the pros and cons for both overseas and local studies.

    Thank you in advance.

    • Dr. Lau

      As long as you can afford, it would be best if you studied abroad. When you study oversea, you get to mix with the locals, learn how they work, experience their culture. I studied Diploma in Singapore for 3 years and worked for 3 years before I further study in Australia, it was a great experience.

      We can evaluate the degree base on course structure and the university QS ranking. The course structure is somewhat similar. Let’s compare Data Science And Analytics by (NUS) vs Bachelor of Computer Science (data science) by UM. Both cover the introduction to data science, mathematics, statistics, data structure, database design. Both also offer electives on business, networking, and some advanced topics.

      Both NUS and UM are recognised globally so you won’t have a problem further study in Master or PhD. Ranking wise NUS is higher and is in the top 1% of world universities. But data scientist is still highly in-demand, and employers assess your skills first.

      There aren’t any obvious cons studying in Singapore. Their school fee is reasonable and international students can apply for a tuition grant. Some might say the living cost is expensive because of the currency, but you can make it up with part-time jobs.

      The only downside studying in Singapore is that their people, culture, environment are like Malaysia. You might want to explore studying in Australia? I can provide some information if you need further details.

  7. Ray

    Hi Dr Lau,

    Which private universities do you recommend for Data Science degree programme?

    a) Monash University – Bachelor of Computer Science in Data Science
    b) Heriot-Watt University – Bachelor of Science (Hons) in Statistical Data Science
    c) Asia Pacific University (APU) – Bachelor of Science (Hons) in Computer Science with a specialism in Data Analytics
    d) Taylor’s University – Bachelor of Computer Science (Hons) with a specialism in Data Science

  8. Chloe Ang

    Hi Dr Lau ,May I ask if you recommend to study statistical data science in the university of Heriotwatt Malaysia?

    • Dr. Lau

      Hi Chloe,

      It depends on your career path. This degree suits someone who wants to become a data analyst. In particular areas like finance, banking, and insurance. Because many of the subjects focus on financial mathematics and actuarial science.

      If you take a closer look at their electives are also related to finance. E.g. finance reporting, portfolio theories, and derivative markets. For the same reason, this course will lay a foundation if you plan to work as a researcher, quant, or pursue a Masters or PhD. degree.

      This degree doesn’t cover typical subjects such as Python programming, data structure, algorithms, database designs, and SQL. You might want to consider other degree programs if you are looking for a more technical career path: E.g. data scientist, big data analyst, machine learning engineer, or AI developer.

  9. Leyla

    Thanks for the beneficial insight. What do you think of the course Bachelor Hons in Data Analytics at Asia Pacific University, Malaysia? Does the syllabus cover the important aspects needed for this career?

  10. Ching

    Hi, for USM elective courses: among business analytics elective and multimodal analytics elective, which one do you recommend?

    Business analytics consists of Consumers Behavioural & Social Media Analytics, Business Intelligence & Decision Analytics, Predictive Business Analytics.

    Multimodal analytics consists of Multimodal Information Retrieval, Text & Speech Analytics and Forensic Analytics & Digital Investigations.

    Thanks in advance.

    • Dr. Lau

      Hi Ching,

      It depends on your career goal. Business analytics is generally more applicable. Most data analyst jobs require BI and predictive analytics. Consumers behavioural and social media analytics are handy in most customer-oriented industries. E.g. retail and e-commerce.

      I chose multimodal analytics during my uni time because I am interested in forensic. Also I like to learn how to analyze structured data since I was already pretty well-versed in software development at that time.

  11. Christina

    Hi Dr. Lau

    I would like to ask which university in Malaysia do you recommend for Master data science programme?

    Personally, I wish to learn more on analytics and modeling.

    Thanks in advance.

    • Dr. Lau

      Hi Christina,

      Have you shortlisted any universities? Let me know the top 3 in your mind and I will be able to guide you. It is rather hard to prescribe a single on given that they all have its own pros and cons. You may also refer to this video for some guidance.

      • Chai Keng Fui

        Hi Dr.Lau, is there a doctoral program in UTAR Kampar for data science? If yes, what is the entry requirement? Is it enough to have degree in mathematics science (Statistics) with a MBA?

  12. Puteri

    Hai, considering Data Science for my degree this year. Not sure what is written for me in the future. Maybe answering some questions in my head will help me put myself to ease

    So, some of my questions are
    -Where can I apply for careers with a Data science degree
    -Is it appropriate to study for masters and PhD right after degree in Data Science, will that help me in securing a career?
    -Is Data science in high demand in 3-5 years from now?

    • Dr. Lau

      1. Most companies are looking to hire data science professionals, from data engineers, data analysts, to data scientists. They advertise on their company career page or job portals.
      2. A master or PhD is not required to secure a data science job unless you want to venture into academic research.
      3. Do you think we will have more data in the next 3-5 years from now? That is your answer 🙂

  13. Oscar

    Hi Dr Lau, TARUC is offering Computer Science Degree In Data Science since 2019. Do you recommend me to enrol to the course and what are the thoughts from you? Below are their programme outline
    Introduction to Computer Systems
    Web Design and Development
    Software Engineering
    Internet of Things
    Introduction to Computer Networks
    Data Science
    Statistics for Data Science
    Artificial Intelligence
    Machine Learning
    Data Engineering
    Business Intelligence
    Data Warehouse Technology
    Cloud Computing
    Problem Solving and Programming
    Database Management
    Advanced Database Management
    Object-Oriented Programming
    Research Methods
    Data Structures and Algorithms
    Social and Professional Issues
    Probability and Statistics
    Discrete Mathematics
    Project I
    Project II
    Industrial Training (6 months)
    Electives (Choose 3):
    Introduction to Computer Security
    Mobile Application Development
    Natural Language Processing
    Graphics Programming
    Image Processing
    Algebra and Calculus
    Music Appreciation
    Introduction to Short Story
    Free Module Electives (Choose 1):
    Business Organization and Management
    Principle of Marketing

    • Dr. Lau

      Hi Oscar,

      Have you watched this video ?

      If you have, I got two questions for you:
      – What is your career goal / plans?
      – If you have not decided, at least tell me what are your interest area, that will help to provide relevant input for your questions.


      • Oscar

        Hi Dr Lau,

        I have definitely watched your video. Data Scientist will be my first career option, follow by Data Analysts. FYI, I am finding tertiary education for Data Science. I am just getting trouble what university that suit me. For now, APU and TARUC are under my consideration. I would like to ask you which university will you recommend me more. Thanks for your comment!

        Btw, this was the syllabus that TARUC lecturer gave me

        Basic and inferential Statistics
        Mathematical concepts (linear algebra and multivariate calculus)
        Classical Machine Learning (supervised and unsupervised)
        Artificial Intelligence (Deep Learning that involves neural networks)
        Visualization and Reporting (using Tableau, QlikView, etc.)
        Big Data Storage (using Hadoop, Hive, etc.)
        Databases (Relational: SQL, MongoDB, Non-Relational: NoSQL, etc.)
        Machine Learning on Cloud (AWS, Azure, etc.)
        Analytical tools (R, Python, Apache Spark, SAS, etc.)
        Real-Time Data Handling (Apache Kafka, Amazon Kinesis, Flink, etc.)
        Data Science Project Management Method (CRISP-DM)

  14. Tasnim

    Hi Dr. Lau,

    I’m considering pursuing my postgraduate study in Data Science. I have done some research and highlighted these two universities under my consideration. Those are:

    1. UM- Master of Data Science (Coursework)
    2. Asia Pacific University- MSc in Data Science and Business Analytics

    Which one do you think is better in terms of the course structure that I can benefit the most once I graduated? As for my career-path concern, I’d like to navigate my path into data science.

    A little bit on my background:
    Recent graduate in Bachelor of Economics (Honours) – 2021
    Equipped predictive analysis for Business and Economics Forecasting – R programming

    Could you advise me on this matter? Thank you so much.

  15. Denise

    Hii , I’m thinking of studying a degree computer science in data science in Monash university malaysia.

    Do you think it’s good to study there in terms of the course ?
    Once I graduate will it be easy to look for a job in Malaysia ?

    • Dr. Lau

      Hi Denise,

      You may refer to this guide on how to choose a university for your data science degree. In general Monash is ok, but you need to check if their curriculum matches your career goals. If you graduate on time, there should still be increasing number of job opportunities from now till 2025.

  16. Chai Keng Fui

    Hi Dr.Lau, is there a doctoral program in UTAR Kampar for data science? If yes, what is the entry requirement? Is it enough to have degree in mathematics science (Statistics) with a MBA?

    • Dr. Lau

      Hi Keng Fui,

      Yes there is. Your bachelor and Master’s degree should be sufficient (subject to your CGPA). You may look into their PhD in Science or Computer Science. Also try to search for the lecturers who are accepting PhD students and look at their research directions. That will help you in the process when you are proposing your research and thesis title.

  17. Michael

    Hi Dr. Lau, I’m currently considering taking Data Science as a second or possibly switch-to Major at WMU (Michigan) but looking at the course structure, it is a 45 credit hour major. I’m a little skeptical about the content of their syllabus (listed below). I would like to become a Data Scientist working with researchers in the medical or astrophysics field mostly. Do you have any advice?

    Elementary Linear Algebra
    R Programming for Data Science
    Storage, Retrieval, and Processing of Big Data
    Machine Learning
    Software Systems Development I: Requirements & Design
    Satisfies Baccalaureate Level Writing Requirement
    Software Systems Development II: Implementation, Testing
    Data Analysis Using R
    Introduction to Mathematical Statistics Using R
    Introduction to Statistical Computing
    Regression Analysis
    Big Data Analysis Using Python

    Applied Data Mining
    Appl Multivariate Stat Method
    Nonparametric Stat Methods
    Time Series Analysis
    Stat Design/Analysis Exper
    Computer Based Data Analysis
    Graphical User Interface Dev
    Intro to Web Technologies
    Computer Security & Info Assurance
    Computer Networks
    Design of User Interfaces
    Artificial Neural Sys
    Parallel Computations
    Comp Modelling/Simulation
    Database Systems
    Database Mgmt Systems

  18. Sam

    Hi, I was wondering what was the base salary a foreigner from EU with a master’s degree in NLP could ask when applying for a Data Scientist position in Malaysia ? Do you think the market in KL has opened more to foreigners ? Is it easy to get sponsored for a work visa as a fresh graduate ? Thank you.

    • Dr. Lau

      Hi Sam, it’s not so much about your degree, it’s more about the company. What are the problems they are solving, what type of talent they are looking for, etc? Typically data scientists in KL gets paid around RM 5,000 – RM 7,000. More companies are looking to hire and expand after 1st May, and KL definitely has more opportunities compared to other states. May I know what is your bachelor degree and do you have any work experience prior to this?



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