Machine Learning Engineer Vs Data Scientist: Who Earns More? – Sent

The world of data, once a quiet and simple corner of the tech world, has exploded in recent years. At the forefront of this revolution are two key roles: the Machine Learning Engineer and the Data Scientist. Both are highly skilled positions with the potential to earn rather appealing salaries. But which is higher? Which of the two roles offers the most raw earning potential? In this post, we’ll discuss the machine learning engineer salary and the data science engineer salary and look into what factors affect the incomes of these two professional fields.

What’s the Difference Between a Machine Learning Engineer and a Data Scientist?

Before we compare the machine learning engineer salary and data science engineer salary, it’s important to understand what exactly these 2 roles involve:

1. Machine Learning Engineer (MLE): An MLE is a bridge between the abstract world of data science and the concrete one of software engineering. They take the models that data scientists build, put them on steroids for real-world use cases, and then make sure they continue to work perfectly within a production environment. The MLE has a strong software engineering background with hands-on experience in building, deploying, and scaling large-scale machine learning systems.
2. Data Scientist (DS): Data scientists are the analytical wizards who extract meaning out of chaos. After all, they clean large datasets to make them amenable for analysis and wrangle/analyze those data looking for insights in trends or cold cases. They construct and evaluate different models of machine learning, selecting the best-performing ones to tackle specific issues. A data scientist requires great statistical and programming skills along with good knowledge of machine learning algorithms.

Machine Learning Engineer Salary Vs Data Science Engineer Salary: Who Earns More?

The average machine learning engineer salary in USA is $125,558 per year. This can range wildly, with junior positions starting at around $80k and senior roles breaching the $180k mark.

The average data science engineer salary is higher than machine learning engineers. The average salary for a data scientist in the USA is $129,189. Just like MLEs, this range can be very wide – entry-level salary expectations for these roles start around $70K and go up to over $190K!

What Factors Affect Salaries of Machine Learning Engineers and Data Scientists?

So, what factors influence the salaries of both professions? Well, as usual, these types of questions are complicated. However, here are the factors that influence the machine learning engineer salary and data science engineer salary:

1. Experience: Like in any field, work experience is a crucial pay factor here. Don’t expect to be taking home the big bucks as a newbie; both entry-level Data Scientists and MLEs can expect pay on the more moderate end of the scale, but these figures rise steadily as experience increases. On the other hand, experienced MLEs and data scientists with robust track records of solid, reliable work can enjoy top-tier salaries, thanks in no small part to the overwhelming demand for both roles.
2. Location: Geography matters. Tech hubs like Silicon Valley or New York City often offer higher salaries than smaller towns or rural areas. The cost of living plays a role, too – higher-paying locations are correspondingly more expensive.
3. Industry: Some industries, such as finance, healthcare, and technology, value expertise in data more and pay accordingly. The non-tech sector may not be as rewarding.
4. Skillset: The more you know and can do with data, the higher your value. For example, being an expert in a particular programming language, a specialized tool, or cutting-edge machine learning methods can positively influence your earnings.
5. Company size and reputation: The rule of thumb is that larger companies and tech giants will generally offer higher wages and better benefits packages.

Additional Factors to Consider When Deciding on a Career Path

While salary is one of the most important factors to consider, it should never be your only deciding factor. Here are some additional points you might want to take into account:

• Style of Work: MLEs have a more engineering-oriented style of work. You can expect to build and maintain systems. Data scientists have a more research-oriented approach, meaning they spend more time deciphering the data. In both cases, a person can achieve high results and earn a lot of money.
• Career Growth: Both fields offer excellent career growth. MLEs can go on to become leaders in machine learning infrastructure or become specialists in deep learning, natural language processing, or other exciting areas. A data scientist can be a data science manager, a specialist in a particular sphere, or even a researcher. Additionally, both fields present numerous Freshers Jobs that provide ample opportunities for entry-level professionals. Therefore, this aspect seems to be equal for both opportunities.
• Job Satisfaction: Think about which skill set you enjoy working with. Are you excited about creating and scaling models (MLE) or discovering data insights based on business needs (DS)?

Final Words

In conclusion, the average data science engineer’s salary is more than the average machine learning engineer’s salary. However, both of their salaries directly depend on their skills, strengths and weaknesses, responsibilities, and company. If neither money nor job preferences can help you choose the best job, ask yourself a question: Do you like exploring data, analyzing its main peculiarities, and thought-provoking results? If “yes,” then you can become a DS. If your answer is “no,” consider the opportunity of becoming an MLE.

The world of data science is full of exciting opportunities for ambitious and passionate practitioners. Whether you take up the mantle of Machine Learning Engineer or a Data Scientist, the most important thing is to keep learning, enjoy your job, and work towards impactful problems.

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