Machine Learning Engineer vs. Data Scientist: A Complete Overview

These two professions sound very different, then why are we comparing them? However, there are many similarities between them, but their paths diverge early enough to delineate real distinctions. The mix of traits, experience, and analytic skills required for this is considered difficult to find, and, thus, the demand for qualified Machine Learning Engineers and Data scientist has exceeded supply in recent years. The demand has led to a surge in people taking up machine learning certifications and machine learning courses globally. 


So, let’s dive into the article in the following order:


  • Who is a Machine Learning Engineer?
  • Who is a Data Scientist?
  • Machine Learning Engineer vs. Data Scientist
  • The Way Ahead


Who is a Machine Learning Engineer?


Machine learning (ML) engineers are knowledgeable programmers who develop machines and systems, which can apply and learn knowledge without a specific direction. Machine learning is a branch of the tree called Artificial Intelligence (AI). AI is the ultimate goal for an ML engineer, and they create programs, which will enable machines to make decisions and take actions without being specifically directed to do the same.


Who is a Data Scientist?


According to Forbes, 2.5 quintillion bytes of data are generated per day, and it is growing at a high rate. Data Science consists of various techniques and tools, which help in understanding data easily. Data Scientists work on complex data problems with their expertise in scientific disciplines. They specialize in a variety of skills like image and video processing, speech, text analytics (NLP), mathematics, etc.


Each of these specialist roles is very limited in number, and hence the value of such a specialist is immense. This has further led people to opt for machine learning training.


Machine Learning Engineer vs. Data Scientist


There’s some confusion surrounding these job roles, primarily because they both are relatively new. However, if we parse things out and examine the semantics, the distinctions can become clear. There are a few parameters that we will consider:

  • Salary Trends
  • Skills Requirements
  • Roles and Responsibilities
  • Job Trends


Salary Trends

The salaries can vary depending on the seniority of the role and the location.

As stated by Forbes, the average salary of a machine learning engineer is around $142,858.57 in 2019.

A data scientist is a much broader role than a machine learning engineer. According to the jobs posted on Indeed, a data scientist role has a median salary of $126,967.


As the demand for and machine learning engineers and data scientists is growing, you can also expect these numbers to rise.


Skills Requirements

Both engineers require extraordinary skills to work proficiently in their respective fields. Although, few of the skills are common, let us briefly look at them –

Programming Languages: The primary requirement is to have a good grip on a programming language, preferably python, as it is easy to learn, and its applications are wider than any other language.

Statistics: It as the discipline of the collection, analysis, interpretation, presentation, and organization of data.

Data Cleaning and Visualization:Data cleaning is the process of ensuring that your data is reliable and useable by consistency checks. It ensures the quality of the data is met for visualization.

Here’s what you might need to get a job as a Machine Learning Engineer, based on recent job posts:

  • Probability and Statistics Modeling
  • Linux SysAdmin skills
  • Experience in vision processing, deep neural networks, Natural Language Processing, and reinforcement learning
  • Experience working with messaging tools like RabbitMQ or Kafka
  • Experience working with distributed systems tools 
  • Understanding of ML Algorithms
  • Competency with infrastructure as code 


Here’s what you might need to be a data scientist (on the job):

  • Strong Statistical and Fundamentals 
  • Experience in data mining techniques (like generalized linear models/regression, boosting, trees, random forests, and social network analysis)
  • Experience in using machine learning techniques such as neural networks, clustering, and decision tree learning
  • Experience using web services like DigitalOcean, Redshift, and Spark
  • 5-7 years of experience in building statistical models and manipulating data sets
  • Experience in analyzing data from third-party providers 
  • Experience in using distributed data and computing tools 
  • Experience presenting  and visualizing data



Roles and Responsibilities

Machine Learning Engineer Roles

  • Design Machine Learning Systems
  • Study and model Data science prototypes with the collaboration of data engineers
  • Research and implement appropriate ML algorithms or tools
  • Develop machine learning applications according to requirements
  • Improve existing machine learning models
  • Perform Statistical analysis and Fine-Tune using Test Results
  • Be in charge of the entire lifecycle 
  • Analyze complex and large data sets to derive valuable insights
  • Research and implement the best practices to enhance existing machine learning infrastructure

Data Scientist Roles

  • Research and develop statistical models for analysis.
  • Data mining using state-of-the-art methods
  • Understand company needs and devise probable solutions by collaborating with engineering or product management teams
  • Develop custom data models and algorithms
  • Build tools and processes to help analyze and monitor performance 
  • Use predictive modeling to enhance and optimize revenue generation and customer experiences

Job Trends

On the one hand, Machine Learning Engineers get more paid than Data Scientist; on the other hand, the demand or the Job openings for a Data Scientist is more than that of an ML Engineer. This is because ML Engineers work on Artificial Intelligence, which is comparatively a new domain.


Way Ahead

Whether you become a data scientist or a machine learning engineer, you will be working at the cutting edge of business & technology. And since the demand for top technology talent outpaces the supply, the competition for bright minds in this space should continue to be fierce for the years to come. So you really cannot go wrong no matter which path you choose.

Looking to prepare for data science roles? Check out Global Tech Council’s Data Science Certification.

If you’re more focused on becoming a machine learning engineer, consider Global Tech Council’s machine learning certification and guide to machine learning for beginners.