Machine Learning Engineer Vs Data Scientist: Comparison to Decode the Differences

ML and AI
April 27, 2023
Ridgeant

Today’s digitized world manages the processing and analysis of petabytes and terabytes of data. A stringent need for efficient professionals like machine learning engineers and data scientists is inevitable. these are two of the most in-demand positions right now, sought by industries worldwide.

There is a constant comparison and competition between the two roles – Machine learning engineer vs Data scientist. With days passing by, the line between them is lessening and there is an overlap of roles and responsibilities, though there are certain demarcations that differentiate the two.

Machine learning engineers and data scientists do work together and gel well in certain areas and yet maintain their individualistic characteristics. They are two important keys to modernized and digitalized business. Both have great career growth and demand in all geographies.

Before we compare them both against each other, let us individually look at their introduction.

Machine Learning Engineer – An Overview

Machine learning is a large market, encompassing the majority of AI software and projects. In line with this, the machine learning market is also the largest segment of the AI market. This market is expected to grow from around 22.6 billion U.S. dollars to nearly 126 billion U.S. dollars by 2025.

Machine learning is an important branch of Artificial Intelligence that manages the creation of automatic machines and models through effective algorithms. These models are then exposed to various datasets for further training, to make them workable in real-time scenarios.

A machine learning engineer is an IT professional/programmer who creates, optimizes, and manages machines and algorithms that can utilize knowledge without directions and help in solving problems depending on data. They work in teams with other engineers or data scientists to leverage huge bulk of data to create models for future predictions.

Data Scientist – An Overview

According to a recent research study, the global data science platform market size & share was valued at USD 95.31 billion in 2021 and is expected to reach around USD 695.0 Billion by 2030, growing at a CAGR of 27.6% during the forecast period.

Data science is the study of data that focuses on data analysis and visualization based on data collected from different data sources. These visualizations assist organizations in getting detailed insights that help in competitive analysis, trends, and patterns, in taking business decisions.

Data scientists are analytical professionals who are apt at analyzing and managing huge amounts of information with the help of modern-day technologies. They use statistical methods, data mining, machine learning, and predictive analytics for converting basic data into insightful and smart information. They solve complicated data issues with their innovative scientific methods like speech, NLP, image and video processing, etc.

Key Comparison Between Machine Learning Engineers Vs Data Scientists

	Machine Learning Engineer	Data Scientist
Introduction	A skilled programmer who uses his skills to create models and algorithms to manage data	A data analytical expert who performs statistical analysis and creates algorithms to be analyzed
Focus Area	Focus on making predictions about future events based on past performance	Focus on finding insights from data sets of varied nature and sizes
Key Objective	Accept the models prepared by data scientists and lead them to production	Create solutions using ML or Deep Learning models for different business issues
Roles and Responsibilities	Create and maintain ML algorithms Use the best of ML principles in business Testing and evaluating models and algorithms on real-world scenarios to create models and process information Evaluate new data sources for training models and features for existing models Ensure that AI systems can manage complicated tasks like facial recognition, pattern-matching, speech synthesis, and language translation Transform Data Science prototypes Extend current ML libraries and frameworks	Leverage different software for data exploration Communicate insights to all non-tech teams too Work with varied data sets for collection and analysis of data with languages like Python or R and other statistical software Assist organizations in using AI to optimize their business practices Ensure seamless integration with machine learning algorithms that can help organizations understand their internal processes better and more effectively Data mining with modernized methods – Deep learning frameworks like TensorFlow, Keras, etc.
Skills	Tools like Git, Jenkins, Docker, Python, R, and C++ Design and implement new algorithms and methods Statistics, data structures, probability, algorithms Prototyping, data modeling Programming skills (Java, SQL, Python, etc.)	Machine learning algorithms, statistical modeling techniques Easy communication with the non-technical team Software development skills with ML techniques Statistics, data visualization, data wrangling, SQL, and NoSQL
Working Method	They operate by enhancing multiple models for latency, throughput, and performance	They operate by analyzing and visualizing data at all stages of the lifecycle
Career Possibilities	Cloud engineer, AI engineer, ML engineer, cloud engineer, etc.	Data analyst, data scientist, data engineer, BI analyst, etc.
Technical Stack	AWS, Azure, GCP Scala, Python, C++ Docker, Kubernetes Git, GitHub, Bitbucket PyTorch, MXNet, JAX	AWS, Azure, GCP Python, R, SQL Spark Git, GitHub, Bitbucket Scikit-learn, Fast.ai, Rapids

As We Wind Up

As the digital revolution spreads its wings and encompasses almost the whole globe, the need for different professional positions like machine learning engineer and data scientist is going to rise exponentially and there is no looking back. AI has its own increasing fan following now and with ML and data science well integrated with AI, the future is bright and demanding.

As we compare ML engineer Vs data scientist, both have their own share of similarities but a lot of differences too, good enough to have two different positions in the IT industry. Both are in demand and there are times when over supersedes the other. There are twists and turns that can be observed in the demand for these IT professionals.

Data scientists and machine learning engineers collaborate very well with each other too. With organizations that look for both capabilities, there is a positive and rewarding output that is garnered. When they work in different teams, harmonizing their activities becomes a little challenging. This challenge can be overcome by implementing good project management tools and taking guidance from experienced service partners.

Some organizations may be looking for machine learning engineers while some may be keen to have data scientists. For those who technically understand their roles and skills individually, it is easy to grasp which role to go in for. But for those who do not technically understand the difference, it is best to route through an experienced IT partner who can help in deciding whom to go for and thereby, even help in hiring relevant positions with efficacy and professionalism.

We @ Ridgeant offer a bouquet of Machine learning and AI consulting services and solutions, that are focused on assisting you to reach your business goals faster for exponential growth. Reach out to us and fulfill your digital requirements, with ease and proficiency.