Data Science vs. Machine Learning Engineer: A Complete Career Guide

Data science is growing rapidly in the technology sector because of various factors such as the exponential increase in data volumes, advancements in automation and AI, and the growing need to make data-driven decisions. According to Binariks, the global data science platform market is projected to reach $174.10 billion in 2025, up from $150.22 billion in 2024.

Despite this growth, many still confuse data science with machine learning engineering, as both involve working with models and data. However, the two differ significantly in their core focus areas and objectives.

This blog will compare data science vs machine learning engineer and help you make the right choice for your career.

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Data Science vs. Machine Learning Engineer: Which Career is Right for You?

Whether you are interested in building AI models or analysing data for insights, it’s important to understand where your true passion lies. Let’s explore the key differences between these two fields to help you make an informed choice.

Area of Comparison	Data Science	Machine Learning
Key Skills Required	SQL Descriptive Statistics Supervised Learning Experiment Design	C++ Advanced Statistical Models Model Deployment A/B Test Implementation
Career Opportunities	Data Analyst Data Scientist Business Intelligence Engineer Data Engineer	Machine Learning Engineer AI Research Scientist Data Scientist with ML Focus Robotics Engineer
Salary	SGD 72,000 to SGD 108,000 a year on average (Data Scientist)	SGD 77,766 to SGD 124,442 a year on average (ML Engineer)
Core Responsibility	Data detection	AI architecture
Demand Across Industries	Healthcare Finance Retail	Tech startups Manufacturing Logistics

Key Skills Required

Both machine learning and data science require a strong foundation in programming, data handling, and mathematics, but they emphasize different areas. Data science primarily focuses on analyzing data and uncovering insights, while machine learning centers around building and optimizing predictive models.

For instance, data scientists often rely on tools like Pandas, NumPy, SQL, and R. In contrast, machine learning engineers typically use frameworks like TensorFlow, PyTorch, and programming languages like C++ and Java for model development and deployment.

Career Opportunities

Both data science and machine learning are lucrative fields, but they lead to different career routes based on your interests and skill sets. If you like to use data to help businesses make informed decisions, you should choose data science.

On the contrary, you should select machine learning to make intelligent systems that can learn and evolve. Data scientists focus more on gathering, interpreting, and analysing vast amounts of data to help organisations make the best decisions. At the same time, machine learning engineers aim to create computers that learn from data and improve on their own over time.

Salary

When comparing machine learning vs. data science salaries, ML engineers usually earn higher compensation than data scientists. This difference stems from machine learning’s specialised and technical nature, which often requires advanced knowledge in algorithms, deep learning, and software engineering.

Core Responsibilities

Data scientists specialise in using statistical modelling, predictive analytics, and data visualisation to uncover actionable insights. On the other hand, machine learning engineers design scalable machine learning models and deploy them into production environments.

Demand across Industries

ML engineer and data scientist roles are in high demand across different sectors. Data scientists are valued in analytics-heavy industries like finance, healthcare, and retail, while ML engineers are preferred in tech-driven fields where models are deployed into products. However, the distinction is fading as many AI teams seek professionals who can perform both functions.

Also Read: Highest Paying Jobs in Computer Science in Singapore

Data Science vs. Machine Learning Engineer – How to Choose the Right Path?

When choosing between a career as a data science engineer vs a machine learning engineer, you should always select based on your area of interest. For example, if you enjoy telling stories through data, choose Data Science engineering, and if you love systems and coding, choose ML engineering. Apart from that, focus on the following factors:

Your current background.
Your preference between engineering and business problems.
The learning style and tools you prefer.

Also Read: Top Python & R Courses for Data Science Beginners in Singapore

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Also Read: Data Science Tools You Should Know in Singapore

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FAQs on Data Science vs. Machine Learning Engineer Career Guide

Q: What is the difference between a Data Scientist and a Machine Learning Engineer?
Ans: Data scientists and machine learning engineers differ in several ways, such as focus, end goals, and skill sets. For example, a data scientist seeks to extract insights from data to guide business decisions, while a machine learning engineer aims to build, maintain, and deploy machine learning models in production environments.

Q: Which role among data science and machine learning has better career growth prospects in 2025 and beyond?
Ans: In 2025 and beyond, machine learning will enjoy better growth than data science because of the increasing demand for AI-powered solutions across different industries.

Q: Is it easier to transition into data science or machine learning?
Ans: If you already have a strong base in programming and statistics, you will find it easier to transition to data science than to machine learning.

Q: Which role among data scientists and ml engineers is more in demand in the job market right now?
Ans: In 2025, machine learning engineers and data scientists will be in high demand. Still, the former are growing better in specific sectors because these sectors increasingly need production-ready AI systems.

Q: Do Data Scientists and ML Engineers earn the same salary?
Ans: No, machine learning engineers earn slightly higher salaries than data scientists because they play a more specialised role than the latter.