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Machine Learning Engineer vs. Data Scientist

While there are many similarities between machine learning engineers and data scientists, their paths diverge early enough to delineate true distinctions.

One way to examine the relationship between the two roles is to picture the handing off of a baton. A data scientist possesses the background and skill set to perform statistical analysis to shape the strategy of a ML initiative. The data scientist will also build the algorithm that's fed to a ML model.

At that point, the machine learning engineer will take over and use his mastery of related software tools to ensure that the models are scalable and properly functioning.


Machine learning engineers typically possess a Master's degree in computer science or a related form of data engineer training. That education, however, is only the foundation and not a guarantee of career success.

Prospective ML engineers should understand machine learning algorithms, have experience in software engineering and a variety of programming languages, and also have a deep understanding of mathematics and experience in data analysis.

It's also beneficial to have significant experience working with big data.

With these skills in hand, the opportunity to work professionally as a ML engineer improves.

When it comes to data science vs machine learning, it's always important to acknowledge that ML exists under the larger umbrella of data science. While both types of professionals may work with a machine learning data model, the ML engineer's training will qualify him to delve deeper into specific training, development and implementation of ML features.


Machine learning engineers and data scientists certainly work together harmoniously and enjoy some overlap in skills and experiences.

But -- at the core -- when it comes to machine learning engineer vs data scientist, the titles of the roles go far in laying out basic differences. Machine learning engineers are further down the line than data scientists within the same project or company. A data scientist, quite simply, will analyze data and glean insights from the data.

A machine learning engineer will focus on writing code and deploying machine learning products.

Of course, machine learning engineer vs data scientist is only the beginning of nuances that exist within relatively new data-driven disciplines.

When it comes to a data career, the areas of specialization and focus are constantly shifting and growing.


Data operations involves an automated, process-driven approach to increasing the efficiency of analytics. DataOps pays particular attention to speed. It focuses on accelerating the time to actionable business insights.

Data engineering, on the other hand, concerns itself more broadly with applications of big data. It occupies a space somewhere between data science and data analysis.


Examining the nuances of the parameters of data science vs data analyst responsibilities, the biggest difference lies in the proximity to the data.

Data analysts are constantly examining data in search of relevant patterns. Data scientists may possess more significant advanced degrees and have the ability to code, model and program at an advanced level.

In the case of a data engineer vs data scientist, it is the data engineers architecture development that allows a data scientist to conduct research with big data.

All of these roles are interconnected and maximized when operating in unison.

Machine Learning and Data Science with Snowflake

Traditional data warehouses limit how much data that data scientists and data analysts can store, access, and analyze. Snowflake’s cloud data platform was built to seamlessly integrate and support the applications data scientists rely on. With unlimited data storage and compute resources, Snowflake can easily scale up, down, or out to meet fluctuating demands. Snowflake integrates with popular data science tools, including DataRobot, Dataiku,, AWS Sagemaker, and Zepl. Snowflake also supports Python, Scala, R, and Java,, as well as Sparkfor machine learning pipelines.