Machine Learning Engineering and Data Science require different combinations of technical skills, which can influence how these roles are compensated. As professionals gain experience, skills such as software engineering, model development and the ability to deploy and maintain machine learning systems can become valuable.
What Does a Data Scientist Do?
A Data Scientist analyses data to identify trends and patterns, develops and tests models to make predictions, and applies machine learning techniques to extract insights from data. They may also visualise findings through charts, dashboards and reports and communicate insights and recommendations to stakeholders.
What Does a Machine Learning Engineer Do?
A Machine Learning Engineer builds, deploys and maintains machine learning systems. Their work involves developing machine learning models, integrating them into software systems, and monitoring and improving their performance.
They may also work on machine learning pipelines, optimise models for production environments, and collaborate with Data Scientists and software engineers to deploy machine learning models into applications.
What Is the Difference Between a Machine Learning Engineer and a Data Scientist?
Data Scientists focus on analysing data, developing and evaluating models, and extracting insights. Machine Learning Engineers, on the other hand, focus on deploying, integrating, monitoring and maintaining machine learning models in production.
How the Compensation Compares: Data Science vs Machine Learning
| Feature | Data Scientist | Machine Learning Engineer |
|---|---|---|
| Primary Output | Data-driven insights and predictive models | Deployed and production-ready machine learning systems |
| Tool Stack | Python, SQL, R, data visualisation and machine learning tools | Python, machine learning frameworks, software development and deployment tools |
| Core Challenge | Analysing complex data and developing reliable models | Deploying, scaling and maintaining machine learning systems |
| Average MY Salary | RM6,200/month | RM8,180/month |
| On-Call Responsibility | Depends on the organisation and role | May apply for production ML systems |
Why Machine Learning Engineers Earn Higher Salaries
1. Higher Technical Skill Requirements
Machine Learning Engineering often requires a combination of machine learning and software engineering skills. In addition to working with machine learning models, professionals may need to build pipelines, integrate models into applications, and work with the technical infrastructure required to deploy and scale them.
2. They Build Production AI Systems
Machine Learning Engineers help turn machine learning models into systems that can operate reliably in the real world. Their work can involve deploying models, integrating them with applications, monitoring their performance and scaling systems to handle production workloads.
3. AI Adoption Is Growing Rapidly
As organisations in Malaysia increasingly use AI, the demand for professionals with specialised AI skills is also growing. PwC’s 2026 Global AI Jobs Barometer found that job postings in Malaysia requiring AI-related skills increased by 7.4%, highlighting growing demand.
4. Software Engineering Skills Increase Market Value
Machine Learning Engineers combine machine learning with software engineering, including programming, systems integration and deployment. These skills enable them to build and maintain the software and infrastructure needed to operate machine learning models in production.
5. Limited Talent Supply
Organisations may face greater challenges in finding professionals with the right combination of technical skills. PwC’s 2026 Global CEO Survey found that 35% of CEOs in Malaysia reported high exposure to persistent skills shortages, highlighting the wider challenge of developing and attracting talent.
Salary Comparison in Malaysia: Machine Learning Engineer vs Data Scientist
| Experience Level | Data Scientist | Machine Learning Engineer |
|---|---|---|
| Entry-Level (0–2 years) | RM4,000–RM6,500/month | RM4,000–RM8,000/month |
| Mid-Level (3–5 years) | RM7,500–RM12,000/month | RM10,000–RM16,000/month |
| Senior (5+ years) | RM12,000–RM18,000/month | RM16,000–RM25,000/month |
Launch Your AI Career with a BSc (Hons) in Computing (Data Science)
Building a career in data science or machine learning requires a strong foundation in programming, software engineering, data science and machine learning. The BSc (Hons) in Computing (Data Science) at LSBF Malaysia, in collaboration with the University of Greenwich, covers these areas.
The programme also includes modules such as Applications in AI and Data Science, Big Data Analysis and Visualisation, and Advanced Topics in Data Science, giving students the opportunity to develop relevant technical and analytical skills.
This blog is written by Godwin Fernandez
FAQs
Machine Learning is not necessarily harder than Data Science, but it can require a stronger focus on mathematics, programming and model development.
Yes, a Data Scientist can transition into a Machine Learning Engineer role by building skills in software engineering, deployment, and machine learning systems.
Python is widely used for data analysis, machine learning and model development. SQL is used to retrieve and work with data stored in databases. R can also be useful, particularly for statistical analysis and data visualisation.
Industries may include technology, finance and insurance, healthcare, retail, manufacturing, and professional services.
Yes, fresh graduates can pursue entry-level Data Scientist roles if they have a relevant educational background and the technical skills required for the position.
SUGGESTED COURSE
Bachelor of science with Honours in Computing (Data Science) 3+0 in collaboration with University of Greenwich
The BSc (Hons) Computing (Data Science) programme is designed to provide students with a strong foundation in data science, machine learning, and information retrieval. It equips you with the ability to apply data-driven techniques and artificial intelligence concepts to analyse complex datasets and solve real-world problems.
