As more organizations are eager to capitalize on the benefits of emerging artificial intelligence trends like computer vision, they’re realizing they need a well-rounded team. To support this need, job opportunities in AI have grown exponentially in recent years.
Previously, the prevailing mentality was, “We need to hire some data scientists,” whereas now, enterprises are starting to see the need for hiring more specialized functions. According to Gartner, the ML engineer role will be the fastest growing in the AI/ML space, and by 2023, there will be five ML engineers for every 10 data scientists.
Recently, enterprises are hiring more ML engineers, as they have a renewed focused on actually putting models into production—a promising sign for the data science world. However, data scientists still play an essential part in development and deployment—putting models into action so they can make impactful business decisions. In this post, we’ll dive into the key differences (and similarities) between machine learning engineers and data scientists and how both roles fit into the broader data science ecosystem.
Key Functions of ML Engineers and Data Scientists
While both machine learning engineers and data scientists are hands-on roles, their day-to-day looks vastly different from one another.
Data scientists are commonly thought of as the builders. They’re responsible for analyzing and understanding specific business problems, feature engineering, developing, selecting, and tuning models, and then generating insights to present to stakeholders.
ML engineers, on the other hand, are mainly focused on taking these models and helping scale them out into production while ensuring adherence to business SLAs. They’re involved in maintenance and monitoring, typically not so much on creation, and are the ones responsible for integrating models into a business’s workflow. In enterprise settings particularly, machine learning engineers are in charge of implementing a risk mitigation strategy and ensuring the models perform optimally, while working toward the defined strategy.
Think of it through this ultra-simplified lens—if data science were a new housing development, data scientists would be in charge of creating tailored blueprints and floorplans and then selling them to developers before production, similar to how data scientists “sell” their ideas for models to leadership. Machine learning engineers would be in charge of getting the homes on the market, handling property upkeep, and ensuring that all of the residents stayed satisfied.
Skills Required of Machine Learning Engineers and Data Scientists
Both roles have separate functions within an organization, but they often possess many of the same skills and work with the same technologies. ML engineers and data scientists are both usually proficient in Python, have a background in mathematics and statistics, and are experienced in machine learning and predictive modeling.
However, data scientists are often required to be more creative in their day-to-day as their goal is to use data to tell a story. They’re typically the ones who interface with stakeholders directly, so they need to know how to present insights and come up with efficient solutions to pressing business problems.
In contrast, ML engineers tend to have more fundamental software engineering skills, and they sit at the crossroads of data science and IT. They have a stronger foundation in data structure, algorithms, and creating deliverable software, and they almost always have a background in advanced computer science.
In terms of coding, as previously mentioned, both personas predominantly use Python. ML engineers typically write low-level code to tweak and optimize default implementations, and they’re also proficient in high-efficiency programming languages such as C++, Java, and Scala. Data scientists write higher-level code with Python or R, and they often use BI tools for data analysis and visualization. While ML engineers’ job revolves around machine learning, machine learning is just one tool in a data scientist’s toolbelt—they might go months relying instead on data analytics and statistics.
Data scientists interested in transitioning to a machine learning engineer should focus on one thing—upskilling. By increasing their knowledge on data infrastructure management and algorithms while collaborating with ML engineers more closely on making models available, impactful, and valuable to users, data scientists will make themselves more attractive candidates.
Average Salary for Data Scientists and ML Engineers
Possibly due to their more technical skillset, when comparing an ML engineer vs. data scientist salary, the average machine learning engineer salary is typically higher. There’s also the influence of supply and demand—less candidates possess the skills needed of an ML engineer, while more organizations are putting an emphasis on operationalizing models.
According to Indeed, the average data scientist in the United States makes $109,802 per year, whereas the average ML engineer earns $132,651 annually. As the demand for both career paths increases, it’s likely that the average salaries for both positions will continue to increase as well.
Bonus: Where Do Data Engineers Fit In?
Data engineers are also deeply technical data workers, but they typically specialize in data pipeline architecture and ensure that the data flows and is delivered properly.
When comparing machine learning engineers vs. data scientists vs. data engineers, data engineers sit at the front of the workflow. They’re the ones in charge of initially preparing the data that data scientists then use to build the models that ML engineers employ for business use cases. On a daily basis, data engineers are more concerned with making data more accessible for users in the organization.
Wrapping Up
As new challenges and opportunities continue to arise in the AI discipline, organizations need to understand how different, highly specialized data-focused roles are designed and how they work together in an ideal environment. The increase in ML engineer demand indicates that more businesses are investing in putting their models into production, which is a great sign!
The next step is implementing a data science platform that can support different roles and their diverse needs, while also driving collaboration between them to maximize productivity and set themselves up for success.
Want to ensure you’ve optimized your team to create and execute on successful AI and ML projects? Check out our ebook, Building the Perfect AI Team, for advice on the best ways to organize your team, necessary roles, and innovative ways technology can support your next AI initiatives.
