Did you know? Based on Fortune Business Insights, the global market for data science platforms is likely to reach US$484.17 billion by 2029, growing at a jaw-dropping CAGR of 29.0% annually! Meanwhile, the machine learning market is on track to hit US$225.91 billion by 2030, with an impressive growth rate of 36.2%. This remarkable growth has caused intense competition among major IT companies, resulting in a hiring frenzy and increased salaries in these high-demand sectors!
Curious to learn more? Keep on reading to know what exactly differentiates these two roles across businesses. We’ll discuss extensively about data scientists vs. machine learning engineers in this blog. Additionally, gain insights into the latest job market trends and possible annual salaries for professionals in these fields.
Table of Contents
Watch this video to understand the difference between Data Science and Machine Learning:
What is a Data Scientist?
A data scientist is an individual who investigates large amounts of data in search of hidden patterns and exclusive hints. They go through data to find unusual patterns and information that others might miss. Data scientists create specialized software, or models, to forecast future occurrences or resolve problems. They make complex data understandable by converting it into graphic representations like charts and graphics. Based on the insights extracted from the data, these results inspire proposals for wise decisions.
As per Forbes, every day, the world sees the creation of more than 2.5 quintillion bytes of data, an astonishing figure that’s constantly on the rise. With such massive data accumulation, organizations need to use it effectively to drive significant business outcomes and maintain competitiveness. This is where data science steps in.
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What is a Machine Learning Engineer?
A machine learning engineer is a programmer who builds large-scale systems handling vast data. They train algorithms to perform tasks, offering insights and predictions. They manage the entire data science process, from data collection and processing to model creation and deployment.
According to a McKinsey study, 35 percent of what consumers purchase on Amazon and 75 percent of what they watch on Netflix come from product recommendations that are based on recommended engines. Many companies use recommendation engines to generate and promote a range of products that customers will be interested in.
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Data Scientist vs Machine Learning Engineer: Demand and Growth
Data Scientist: Demand and Growth
When it comes to the demand for data scientists, the US Bureau of Labor Statistics projects a 35% increase in jobs for data scientists between 2022 and 2032—a rate that is substantially higher than the average for all occupations. This corresponds to about 17,700 new positions each year.
Google Trends graph below shows the internet search for “data scientist” is going up and up! A continuous increase in the number of Google searches for the keyword “Data Scientists” over the years. This trend is reflected to the market’s increasing demand and growth for data scientists.
Source: Google Trends
Machine Learning Engineer: Demand and Growth
According to the World Economic Forum’s Future of Jobs Report 2023, demand for AI and ML expertise is expected to increase significantly. It projects a 40% growth, or about 1 million new jobs, by 2027, primarily due to the growing application of ML in various industries.
Below Google Trends graph, showing the year-on-year rise in Google searches for “Machine Learning Engineer.” This trend underscores the increasing demand for machine learning engineers in the upcoming years.
Source: Google Trends
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Roles and Responsibilities: Data Scientist vs Machine Learning Engineer
Data scientists and machine learning engineers often collaborate on projects and primarily, both work with data, but they have different approaches and focus areas. Data scientists uncover insights and build models, while machine learning engineers implement these models into real-world applications.
Let’s now study more in detail data scientist roles and responsibilities vs machine learning engineer roles and responsibilities.
Responsibilities as a Data Scientist
- Data Wrangling and Cleaning: Prepare data for analysis by identifying and removing errors, inconsistencies, and biases.
- Exploratory Data Analysis (EDA): Analyze data to understand its characteristics, identify patterns, and formulate hypotheses.
- Statistical Modeling: Develop and apply statistical models to understand relationships between variables and make predictions.
- Machine Learning Model Development: Design, train, and evaluate machine learning models for specific tasks.
- Data Visualization: Communicate insights effectively through data visualizations (charts, graphs).
- Communication: Collaborate with stakeholders to understand business needs and communicate findings in a clear and actionable way.
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Responsibilities as a Machine Learning Engineer
- Build Machine Learning Models: Machine learning engineers will work on the key highlights provided by data analysts and build ML Models alongside data pipelines to create effective business solutions.
- Model Deployment: Develop production-grade pipelines for deploying and serving machine learning models.
- Fine Tuning Models: Ensure models are efficient, scalable, and perform well in production.
- Monitoring Model Performance: Track model performance and identify potential issues.
- MLOps: Automate end-to-end machine learning lifecycle
Now, let’s see the skills you require to handle these responsibilities as a Data Scientist or Machine Learning Engineer.
Watch this Data Science Course video to understand the basic concepts of data science:
Skills Required: Data Scientist vs. Machine Learning Engineer
While both roles involve working with data and machine learning techniques, let’s explore the key differences in the skills required for Data Scientists vs. Machine Learning Engineers:
| Skills | Data Scientist | Machine Learning Engineer |
| Programming Languages | Python, R, SQL, and potentially Scala | Python, SQL, C++ (for performance optimization), and Java or Scala (for distributed systems) |
| Machine Learning | Strong understanding of various algorithms (classification, regression, clustering, etc.), cross validation | Deep understanding of specific algorithms used in a project, model optimization, and fine tuning |
| Data Engineering | Data cleaning, wrangling, transformation, and ETL pipelines | Data pipelines, distributed systems (e.g., Hadoop, Spark), and cloud computing platforms |
| Statistics & Math | Strong foundation in statistics, probability, hypothesis testing, and the ability to interpret results | Solid understanding of statistics, linear algebra, calculus, and optimization techniques |
| Communication & Collaboration | Ability to explain complex concepts to technical and non-technical audiences, and also, work effectively across teams | Clear and concise communication with technical teams, as well as collaboration with stakeholders to define project goals |
| Other Skills | Version control (Git), data visualization tools (Tableau, Power BI), and basic understanding of cloud platforms | MLOps, software testing frameworks, and infrastructure automation tools |
Learn about Data Science vs Machine Learning for in-depth knowledge and career growth.
Jobs Available: Data Scientist vs. Machine Learning Engineer
Jobs Available for Data Scientist
Right now, if you search for data analyst jobs available in India, you will find out that on just LinkedIn, there are 1,15,000+ jobs available. Whereas if you search for data analyst jobs in the USA, you will find out that there are 1,90,000+ jobs available.
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Jobs Available for Machine Learning Engineers
If you go and search for machine learning engineer jobs available in India, you will discover over 23,000+ jobs available, just on LinkedIn. Whereas if you browse for data analyst jobs in the USA, you will find out that there are 63,000+ jobs available.
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Salary Comparison: Data Scientist vs. Machine Learning Engineer
Data Scientist Salary
Let’s have a look at the salaries for Data Scientists in India and the United States. The table below clearly summarizes the compensation details received from Glassdoor.
| Salary of a Data Scientists |
| Range | In India | In United States (US) |
| Low | ₹ 8,00,000/annum | US$ 80,300/annum |
| Average | ₹ 13,00,000/annum | US$ 124,000/annum |
| High | ₹ 20,50,000/annum | US$ 191,880/annum |
The compensation for Data Scientists might differ depending on a number of criteria, including region, experience level, and the hiring organization. These numbers are just combined averages. Salaries in India can reach up to ₹22 lakhs per year, while in the US they can go up to US$ 200k per annum.
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Machine Learning Engineer Salary
Let’s have a look at the salaries for Data Scientists in India and the United States. The table below clearly summarizes the compensation details received from Glassdoor.
| Salary of a Machine Learning Engineer |
| Range | In India | In United States (US) |
| Low | ₹ 7,00,000/annum | US$ 103,400/annum |
| Average | ₹ 11,00,000/annum | US$ 162,750/annum |
| High | ₹ 14,00,000/annum | US$ 253,000/annum |
The compensation for Machine Learning Engineer also might differ depending on a number of criteria, including region, experience level, and the hiring organization. These numbers are just combined averages. Salaries in India can reach up to ₹16 lakhs per year, while in the US they can go up to US$ 256k per annum.
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FAQs: Data Scientist vs. Machine Learning Engineer
Can a data scientist become an ML engineer?
Yes, without a doubt, a data scientist can become an ML engineer. A data scientist is essentially someone who builds models and algorithms for problem-solving using machine learning. These models and algorithms produce insights that can be utilized by businesses and other organizations. Having experience working with ML models and algorithms helps them easily transition to a role as an ML engineer.
Are ML engineers in demand?
In recent years, there has been a sharp increase in demand for machine learning professionals. Machine learning engineers are in great demand from businesses, ranking as the fifth most in-demand career in 2023.
Which pays more data science or machine learning?
On average, machine learning engineers earn slightly more than data scientists, with salaries ranging from US$120,000 – US$150,000. On the other hand, compared to data scientists, US$ 90,000 – US$130,000. However, both roles offer good compensation, with particular values, depending on criteria such as experience, region, and sector.
Is data science easier than ML?
Data science focuses on data processing and analysis, but machine learning requires more software engineering and mathematical understanding for model development and deployment. Since each demand has distinct skill sets, both require continuous learning and technological adaptation.