1.1.3.3 Machine learning engineer vs. data scientist

ML engineers might spend most of their time wrangling and understanding data. This leads to the question: how is a data scientist different from an ML engineer?

There are three reasons for much overlap between the role of a data scientist and the role of an ML engineer.

First, according to Wikipedia, “data science is a multidisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from structured and unstructured data.” Since machine learning models learn from data, machine learning is part of data science.

Second, traditionally, companies have data science teams to generate business insights from data. When interests in ML were revived in the early 2010s, companies started looking into using ML. Before making a significant investment in starting full-fledged ML teams, companies might want to start with small ML projects to see if ML can add value. A natural candidate for this exploration is the team that is already working with the data: the data science team.

Third, many tasks of the data science teams, including demand forecasting, can be done using ML models. This is also how most data scientists transition into ML roles.

However, there are many differences between ML engineering and data science. The goal of data science is to generate business insights, whereas the goal of ML engineering is to turn data into products. This means that data scientists tend to be better statisticians, and ML engineers tend to be better engineers. ML engineers definitely need to know ML algorithms, whereas many data scientists can do their jobs without ever touching ML.

As a company’s adoption of ML matures, it might want to have a specialized ML engineering team. However, with an increasing number of prebuilt and pretrained models that can work off-the-shelf, it’s possible that developing ML models will require less ML knowledge, and ML engineering and data science will be even more unified.