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Theres some confusion surrounding the roles of machine learning engineer vs. data scientist, primarily because they are both relatively new. However, if you parse things out and examine the semantics, the distinctions become clear.

At a high level, were talking about scientists and engineers. While a scientist needs to fully understand the, well, science behind their work, an engineer is tasked with building something.

But before we go any further, lets address the difference between machine learning and data science.

It starts with having a solid definition of artificial intelligence. This term was first coined by John McCarthy in 1956 to discuss and develop the concept of thinking machines, which included the following:

Approximately six decades later, artificial intelligence is now perceived to be a sub-field of computer science where computer systems are developed to perform tasks that would typically demand human intervention. These include:

Machine learning is a branch of artificial intelligence where a class of data-driven algorithms enables software applications to become highly accurate in predicting outcomes without any need for explicit programming.

The basic premise here is to develop algorithms that can receive input data and leverage statistical models to predict an output while updating outputs as new data becomes available.

The processes involved have a lot in common with predictive modeling and data mining. This is because both approaches demand one to search through the data to identify patterns and adjust the program accordingly.

Most of us have experienced machine learning in action in one form or another. If you have shopped on Amazon or watched something on Netflix, those personalized (product or movie) recommendations are machine learning in action.

Data science can be described as the description, prediction, and causal inference from both structured and unstructured data. This discipline helps individuals and enterprises make better business decisions.

Its also a study of where data originates, what it represents, and how it could be transformed into a valuable resource. To achieve the latter, a massive amount of data has to be mined to identify patterns to help businesses:

The field of data science employs computer science disciplines like mathematics and statistics and incorporates techniques like data mining, cluster analysis, visualization, andyesmachine learning.

Having said all of that, this post aims to answer the following questions:

If youre looking for a more comprehensive insight into machine learning career options, check out our guides on how to become a data scientist and how to become a data engineer.

As mentioned above, there are some similarities when it comes to the roles of machine learning engineers and data scientists.

However, if you look at the two roles as members of the same team, a data scientist does the statistical analysis required to determine which machine learning approach to use, then they model the algorithm and prototype it for testing. At that point, a machine learning engineer takes the prototyped model and makes it work in a production environment at scale.

Going back to the scientist vs. engineer split, a machine learning engineer isnt necessarily expected to understand the predictive models and their underlying mathematics the way a data scientist is. A machine learning engineer is, however, expected to master the software tools that make these models usable.

Machine learning engineers sit at the intersection of software engineering and data science. They leverage big data tools and programming frameworks to ensure that the raw data gathered from data pipelines are redefined as data science models that are ready to scale as needed.

Machine learning engineers feed data into models defined by data scientists. Theyre also responsible for taking theoretical data science models and helping scale them out to production-level models that can handle terabytes of real-time data.

Machine learning engineers also build programs that control computers and robots. The algorithms developed by machine learning engineers enable a machine to identify patterns in its own programming data and teach itself to understand commands and even think for itself.

When a business needs to answer a question or solve a problem, they turn to a data scientist to gather, process, and derive valuable insights from the data. Whenever data scientists are hired by an organization, they will explore all aspects of the business and develop programs using programming languages like Java to perform robust analytics.

They will also use online experiments along with other methods to help businesses achieve sustainable growth. Additionally, they can develop personalized data products to help companies better understand themselves and their customers to make better business decisions.

As previously mentioned, data scientists focus on the statistical analysis and research needed to determine which machine learning approach to use, then they model the algorithm and prototype it for testing.

Springboard recently asked two working professionals for their definitions of machine learning engineer vs. data scientist.

Mansha Mahtani, a data scientist at Instagram, said:

Given both professions are relatively new, there tends to be a little bit of fluidity on how you define what a machine learning engineer is and what a data scientist is. My experience has been that machine learning engineers tend to write production-level code. For example, if you were a machine learning engineer creating a product to give recommendations to the user, youd be actually writing live code that would eventually reach your user. The data scientist would be probably part of that processmaybe helping the machine learning engineer determine what are the features that go into that modelbut usually data scientists tend to be a little bit more ad hoc to drive a business decision as opposed to writing production-level code.

Shubhankar Jain, a machine learning engineer at SurveyMonkey, said:

A data scientist today would primarily be responsible for translating this business problem of, for example, we want to figure out what product we should sell next to our customers if theyve already bought a product from us. And translating that business problem into more of a technical model and being able to then output a model that can take in a certain set of attributes about a customer and then spit out some sort of result. An ML engineer would probably then take that model that this data scientist developed and integrate it in with the rest of the companys platformand that could involve building, say, an API around this model so that it can be served and consumed, and then being able to maintain the integrity and quality of this model so that it continues to serve really accurate predictions.

To work as a machine learning engineer, most companies prefer candidates who have a masters degree in computer science. However, as this field is relatively new and there is a shortage of top tech talent, many employers will be willing to make exceptions.

Related: How to Build a Strong Machine Learning Resume

However, to stand a chance, potential candidates need to be familiar with the standard implementation of machine learning algorithms which are freely available through APIs, libraries, and packages (along with the advantages and disadvantages of each approach).

According to a report by IBM, machine learning engineers should know the following programming languages (as listed by rank):

Heres what youll need to get the job, based on current job postings:

Like machine learning engineers, data scientists also need to be highly educated. In fact, many have a masters degree or a Ph.D. Based on one recent report, most data scientists have an advanced degree in engineering (16 percent), computer science (19 percent), or mathematics and statistics (32 percent).

Related: A Guide to Becoming a Data Scientist

That being said, according to Paula Griffin, product manager at Quora, There are large swaths of data science that dont require [advanced degree] research-oriented skills. Theres a huge amount of impact that you can have by leveraging the skills that are better built through industry settings as well.

(Source.)

Heres what youll need to get the job:

The responsibilities of a machine learning engineer will be relative to the project theyre working on. However, if you explore the job postings, youll notice that for the most part, machine learning engineers will be responsible for building algorithms that are based on statistical modeling procedures and maintaining scalable machine learning solutions in production.

Heres what these roles typically demand:

To get an idea of the variance of machine learning engineering jobs, we took a look at job postings on several different sites.

Heres a recent posting for a New York City-based machine learning engineer role at Twitter:

(Source.)

Heres a recent posting for a San Francisco-based machine learning engineer role at Adobe:

(Source.)

When compared to a statistician, a data scientist knows a lot more about programming. However, when compared to a software engineer, they know much more about statistics than coding.

Data scientists are well-equipped to store and clean large amounts of data, explore data sets to identify valuable insights, build predictive models, and run data science projects from end to end. More often than not, many data scientists once worked as data analysts.

Heres what the role typically demands:

Heres a recent posting for a New York City-based data scientist role at Asana:

(Source.)

Heres another recent posting for a San Francisco-based data scientist role at Metromile:

(Source.)

The wages commanded by machine learning engineers can vary depending on the type of role and where its located. According to Indeed, the average salary for a machine learning engineer is about $145,000 per year.

What data scientists make annually also depends on the type of job and where its located. Remember, it is a much broader role than machine learning engineer. That said, according to Glassdoor, a data scientist role with a median salary of $110,000 is now the hottest job in America.

As the demand for data scientists and machine learning engineers grows, you can also expect these numbers to rise.

Related:Machine Learning Engineer Salary Guide

If you take a step back and look at both of these jobs, youll see that its not a question of machine learning vs. data science. Instead, its all about what youre interested in working with and where you see yourself many years from now.

Lets summarize the questions posed at the beginning of this article:

Whether you become a machine learning engineer or a data scientist, youre going to be working at the cutting edge of business and technology. And since the demand for top tech talent far outpaces supply, the competition for bright minds within this space will continue to be fierce for years to come. So you really cant go wrong no matter which path you choose.

Looking to prepare for broader data science roles? Check out Springboards Data Science Career Track. Its a self-guided, mentor-led bootcamp with a job guarantee!

If youre more narrowly focused on becoming a machine learning engineer, consider Springboards machine learning bootcamp, the first of its kind to come with a job guarantee.

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Machine Learning Engineer vs. Data Scientist | Springboard ...