ML Engineer vs Data Scientist: Whom Should You Hire?
By: Somraj Saha
Tags: Machine Learning, Information Technology, Software Development, Business, Data Science

Why Does a Clear Distinction Between These Roles Even Matter?

The plain & simple answer to the question is Division of Labour. As Adam Smith among other prominent political economists of that time, before he & after as well, stated, the improved efficiency & increase in greater skill gained from division of labour is hard to pass on. From an entrepreneur’s perspective, this sounds like a free lunch.

Often businesses operate under tight financial constraints. Hence, having an ML Engineer focus on only one task, say ensuring the data science models are production-ready, saves both time & money. This way the engineer can take care of any blocking issues. And I’ll not have to worry about being that boss who switch contexts at a whim.

Beside the not-so obvious benefits from a business perspective, there’re other benefits as well. Some which you might or mightn’t have realized already.

A clearly defined set of responsibilities, work in favor of the employees to better understand the goals of the project. They can then set aside time & resources as required to achieve that goal. Besides, a major factor to consider is, not to burden the employees with overwork.

A defined designation of roles does have many obvious added advantages then. So if that’s how it is, what specific work should the Data Scientist and/or the ML Engineer be assigned to? In other words, how do you define responsibilities for that job position?

The next section attempts to answer these questions.

Who’re ML Engineers Or Data Scientists & What Do They Do?

This article is one of many similar articles on the same topic “Machine Learning Engineer vs Data Scientists”. Those articles often describe their respective responsibilities of those roles. But the descriptions are factual & only explain job descriptions, that’s it, nothing more & nothing less. None of those contents explain the specifics that define the two job responsibilities.

One such article I stumbled upon & also the one which inspired me to write this article is — Machine Learning Engineer vs. Data Scientist on the Springboard Blog by Andrew Zola. The article is factual as expected, but sheds enough light onto the differences. In other words, a perfect article, brief enough for anyone to quickly grasp the differences between a Data Scientist & a Machine Learning Engineer.

Just so you don’t have to go through the article, here’s a brief summary of the article.

The author mentions ML Engineers as individuals who leverage big data frameworks. Besides, their in-depth coding skills are also used to build intelligent programs. They’re also responsible for productionising theoretical data science models into practical applications.

Fair enough. That’s very atypical of any engineer from any background regardless of their domain.

To reiterate on his definition, the ML Engineer is like any other traditional software developer. But the only extra skills are those which are necessary to work within an ML environment. While Data Scientists are individuals who look into all aspects of the business, to develop programs (not necessarily software) as per the requirements.

In other words, a Data Scientist handles tasks more akin to what a typical analyst has been doing for ages. “Data Scientists” is just a fancy term (or designation) given to them only after the recent AI hype.

It’s not uncommon to find Data Scientists with only limited programming knowledge. After all, querying a SQL Database , making some visualizations using Python/R are often all the tasks they are ever expected to do. At most, they’re responsible for designing the data science model for implementation into a production-ready app.

At a glance, the two roles have very distinct & defined roles, yet employers don’t seem to care about them. Could it be a simple coincidence? Or maybe the employer didn’t know what skill set is needed to solve a business problem?

To get a better understanding of the situation, let’s take a look at some job descriptions. And the following section will do exactly that.