Data Scientist vs Machine Learning Engineer – Similarities & Differences

Data Scientist vs Machine Learning Engineer – Similarities & Differences

Data Scientist vs Machine Learning Engineer – Similarities & Differences

Data Scientist vs Machine Learning Engineer: An Overview

Confused as to what is the difference between a data scientist vs machine learning engineer? They share some similarities, but also have distinctive differences. These in-demand tech professions are quite different from one another.

In this article, we’ll take a closer look at both jobs and then compare them against each other.

Data Scientist vs Machine Learning Engineer: A breakdown of each role

What does a data scientist do?

A data scientist compiles, analyzes, and interprets large quantities of data, then derives actionable insights from their work. Essentially, they are lab scientists that experiment and work with information rather than specimens. The modern data scientist role combines elements of math, statistics, science, and computer science into something new.

Key responsibilities

The day-to-day responsibilities of a Data Scientist may include sourcing data, testing data sets, building custom models and algorithms, tracking the outcomes of predictive models, developing ways data can be used to obtain business outcomes, and consulting with key company stakeholders.

Required skills and qualifications

  • Strong math background
  • Understanding of statistics
  • Knowledge of machine learning
  • Ideally a degree in math, engineering, statistics, or computer science
  • Familiarity with DigitalOcean, Redshift, and other web services
  • Familiarity with MySQL, Spark, and other computing tools

What does a machine learning engineer do?

A machine learning engineer serves as a bridge between software engineering and data science. Their primary role is to add data into the models generated by the data scientists and their teams. To do this, they use programming frameworks that turn raw data into workable data sets.

Key responsibilities

Machine learning engineers are less focused on analyzing and interpreting data than data scientists. Instead, they focus on creating algorithms that train computers and/or machines to do the leg work for them (this is the titular machine learning).

Required tools and qualifications

  • Proficiency with MATLAB and/or other programming tools
  • Experience with distributed systems tools
  • Strong analytical skills
  • Knowledge of machine learning best practices
  • Advanced degree in computer science, mathematics, or statistics

Data Scientist vs Machine Learning Engineer: Final Thoughts

At the end of the day, there is one similarity shared between data scientists and machine learning engineers – both can expect to earn $110,000 to $150,000 per year in the United States. If you’re looking for a new career, choosing between Data Scientist vs Machine Learning Engineer often boils down to a matter of preference. Both are outstanding options.

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