Machine Learning Engineer vs. Data Scientist Guide in 2023
Machine learning is a rapidly expanding field and one of the fastest-growing roles in the artificial intelligence space. Experts suggest that by 2023, there will be about one machine learning engineer for every two data scientists.
If you’re making a career change or figuring out how to specialize, there are quite a few options.
IT encompasses so many aspects that it can be difficult to figure out what each type of job focuses on. One of the more commonly confused pairings is the machine learning engineer and the data scientist.
Although they may work on the same projects, their specific roles differ. Let’s discuss the differences between a machine learning engineer and a data scientist.
What Is a Data Scientist?
Both data scientists and machine learning engineers often work on the same projects at the same company. However, where they are in the line of work is based on their specific job roles (2023 update).
For example, a data scientist works on higher-level tasks. They analyze data and business problems and determine what insights they can take from them. This data can then be used to propel the project forward.
Data scientists may also develop and test models. This means they can see what works and gather the information to make informed decisions. You can think of them as the builders of the project.
In the past, data scientists handled a lot of the work associated with programming and related projects. However, as technology has developed, organizations have focused on hiring for more specific roles.
What Is a Machine Learning Engineer?
Meanwhile, a machine learning engineer works on more nuanced parts of the project and usually begins work later in the process. They would research and design the system, write code, and deploy the actual machine learning projects. In general, their work tends to be more focused on software engineering.
A big part of a machine learning engineer’s job is creating algorithms that allow the machine to learn and make predictions. They then serve as a bridge between the statistics coming from data scientists and the actual construction of the AI systems.
Many experts estimate that there will be a large increase in the demand for ML engineers. Previously, many companies hired data scientists. However, there has been a switch to hiring more specialized roles. This is because this will allow companies to get their products out of the lab and into the market.
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Even though machine learning engineers and data scientists have separate roles, their paths often cross. In fact, they often work on teams with other administrators, analysts, architects, and engineers.
A data scientist would be the one to form the strategy of a machine learning project. He is the one with the skills needed to complete statistical analysis.
Then, the machine learning engineer will finish the job. He has more skills related to checking that models are functional and scalable.
Machine learning falls under the umbrella of data science. They may both work on the same models, but the ML engineer has additional training that lets him work on a further, deeper development of the project.
How to Become a Machine Learning Engineer
Becoming a machine learning engineer takes a lot of study and work. Often, they hold a master’s degree in computer science or a similar form of training. However, education is just the beginning.
Machine learning engineers must continue developing skills unique to ML projects. This includes mathematics, data analysis, and machine learning algorithms. They also need to be competent in software engineering and multiple programming languages.
How to Become a Data Scientist
Working towards a position as a data scientist is not too far out of reach. This is one of the more accessible roles available, even without a formal degree or an extensive job history.
You can become a data scientist by working on your required technical skills. This includes becoming proficient in at least one programming language and improving your skills as you go.
Luckily, there are a few routes you can take to become a data scientist. One of the most popular is to self-study through a course. It’s critical that you choose one that is high-quality and well-structured.
As you learn more, you can work on real-world projects and use them as part of your portfolio. Soon enough, you’ll be well on your way to becoming a data scientist.
Machine learning engineers often have higher salaries than data scientists. This is a reflection of their more advanced skillset. In addition, fewer candidates are generally available to fill these roles even though companies are increasing their emphasis on them.
According to the US Bureau of Labor Statistics, on average, a data scientist in the US earns about $100,910 per year, while an ML engineer earns $132,651. It’s expected that salaries for both positions will increase as demand continues to rise.
Sometimes data-related roles get lumped together. While they are interconnected and reliant on each other, it’s important to understand some key differences.
Data Engineering and Data Operations (2023 update)
Data engineering is a broad application of big data. It essentially falls between data science and data analysis.
On the other hand, data operations, or DataOps, is more specific and focused on improving analytics efficiency. It’s a process-driven, an automated approach focused on increasing the speed to form actionable business insights.
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Data Science and Data Analysis
The difference between these two roles comes from how closely they work with the data. Analysts search through data to find relevant patterns. Meanwhile, scientists code, program, and model at a more advanced level. They, therefore, tend to have more advanced degrees.
Through the engineer’s architecture development, the scientist can perform the research they need to.
Understanding Machine Learning Engineer vs. Data Scientist
The difference between a machine learning engineer and a data scientist may not be immediately apartment to many people. After all, there is a lot of overlap in what they do.
However, once you understand this, you’ll be better equipped to continue progressing in your career path.
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