Machine Learning Engineer vs Data Scientist: What’s The Difference?

Alexei Falco /
 on 20 August 2019

In order to efficiently build the data-driven software, a company needs knowledgeable specialists who have experience in working with the data and corresponding tools.

The roles of machine learning engineer and data specialist are relatively new so many people still confuse them. We will give you an overview of each role and will also see the responsibilities of each.

Machine learning and data science: defining the fundamentals

Before talking about the roles of machine learning engineer and data scientist, we first need to be clear on what exactly machine learning and data science are.

Machine learning

In simple words, machine learning technology uses data-driven algorithms so that a machine (software application) can learn and draw assumptions based on the data.

The more data is “fed” to the machine, the better the predictions are. So if the data is well-processed, predictions will be more accurate.

The most common examples of ML in real life would be personalized recommendations (in online stores, for example) or predictive analytics that is used by banks or lending companies to assess whether the borrower is creditworthy.

Data science

Data science analyzes the data and draws a causal inference based on the results. Data science is aimed to help businesses understand their current state, find reasons for something that happened in the past and come up with the best possible solutions for the future.

In short, data science examines the data and machine learning deploys the results of this examination.

Data scientist: the role and main responsibilities

As the name implies, data scientist does science. These specialists study all aspects of your business processes and define the data science models that will later be used by machine learning engineers.

Data scientists are focused on statistical analysis and research. It helps them come up with the best ML approach to use in the project. As well, data scientists model the data-driven algorithms and prototype them to be tested later.

Requirements

As said above, the role of a data scientist is relatively new and there are no universal requirements as each company will have its own requirements. However, some things are commonly demanded:

  • knowledge and experience with Java/Python;
  • knowledge of database models;
  • experience with web services like DigitalOcean, S3, Spark, Redshift;
  • strong mathematical and analytical skills;
  • experience in building statistical models and manipulating data sets;
  • experience with distributed data;
  • experience with Hadoop, MySQL, Hive, Gurobi.

And these are not all. The requirements for the data scientist will vary depending on your project and business goals.

Machine learning engineer: the role and main responsibilities

Once the data scientist develops the data science models, machine learning engineer will then feed the data to these models. As well, machine learning engineers take the theoretical models provided by scientists and turn them into production-level models that can handle real-time data load.

Another thing that machine learning engineers do is building programs that would control robots (i.e. chatbots) and teaching the machines to learn.

Requirements

Same as with data scientist requirements, the ones for a machine learning engineer also feature some must-have skills:

  • knowledge of Python/R/Java;
  • knowledge of database models;
  • experience with deep neural networks, vision processing;
  • experience with MATLAB;
  • experience with distributed systems.

Again, machine learning encompasses lots of aspects and you will need to find a specialist in accordance with your project.

Summing up

The data scientist and ML engineer support each other but do not have to work together to deliver results. As well, one specialist can be both an ML engineer and a data scientist (which is quite rare).

Data is the new oil and the opportunities for the practical use of both ML and data science basically have no limits — it all depends on one’s creativity, ideas, and resources.

The application of ML and data science in the company’s processes can bring workflow automation, accurate and data-based decision making, precise predictive analytics models, and much more. But work with machine learning and data science demands not only a significant financial investment but also a high level of knowledge and expertise from the specialists who will be working on the tasks.

Authored by  Alexei Falco /
Co-founder of Celadon Software House, manager of projects, has over 8 years of business management and entrepreneurship experience.

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