Data Scientist vs. Machine Learning Engineer – So you want to get started in data science but aren’t really sure exactly what you want to be? A data scientist or a machine learning engineer? Then you’ve come to the right place. Today we’re going to talk about five key differences I wish I knew before I got started. Previously we talked about five things I wish I knew before becoming a data scientist. Make sure to read that first before you move on to this article they’ll help you understand exactly what data science is all about.
Roles Between a Data Scientist and a Machine Learning Engineer
Let’s talk about the difference in roles between a data scientist and a machine learning engineer. Data scientists are typically in charge of data driven problem solving for business use cases. Most of their time will go into research and development, engaging in proof-of-concept projects and they’ll also deal with stakeholders and customers externally and also within the company.
As opposed to this, machine learning engineers will typically be responsible for taking proof of concept to scale. Most machine learning engineers will also tend to have a skew towards software engineering. It’s important to note that you won’t find machine learning engineers in all organizations because it really depends on what stage of the data science cycle a company is in.
If they’re just starting off they might just have data scientists that do everything end-to-end. It’s only when a company decides to invest heavily into data science that it might need machine learning engineers to take their products to scale.
The second difference is in education. More often than not, data scientists come from a variety of backgrounds. Most data scientists have a stem background. They might have done maths, physics, engineering or computer science as a degree at university whereas machine learning engineers tend to be a little bit more skewed towards software engineering.
So more often you’ll definitely find software engineers that have made the transition into machine learning engineer but it is uncommon to see people that have just done a maths or physics degree in machine learning engineering roles.
The Tech Stack
The third difference is in the tech stack used by a data scientist versus a machine learning engineer it’s important to highlight where data scientists and machine learning engineers sit on that data science cycle. So initially any project will usually be started by data scientists and they’ll be conducting a proof of concept to validate a certain approach. If they think that the idea’s got some legs they’ll usually palm off the solution to machine learning engineer who’ll work to deploy it at scale.
In terms of codings data scientists primarily use python in tools such as jupyter notebook which allows for rapid prototyping of ideas but it’s not really best practice if you want to deploy to scale now there’s edge cases to this i know that netflix does have tools that allow them to deploy notebooks in port but that’s definitely an exception rather than the rule then if you look at machine learning engineers they’ll also be using python in combination with apache spark, scala, golang, kubernetes to deploy solutions to scale.
So the role of a machine learning engineer is definitely more technically rigorous when it comes to using software tools whereas a data scientist is more adept at analyzing data.
The Difference in the Mindset
The fourth thing I wish I knew was the difference in the mindset between a data scientist and a machine learning engineer because data scientists are usually at the beginning of the data science cycle they are more responsible for exploration and research and development so they’re more thinking on the model level whereas machine learning engineers will be more focused on the robustness of the code and the efficiency of it additionally they can also be responsible for designing labelling systems, model tests and also experimentation at scale.
The Stakeholder Management
The final difference is in the stakeholder management. Data Scientists are typically the ones that deal with customers and conduct presentations. If you value that customer time and that interaction, I definitely recommend being a data scientist because machine learning engineers on the other hand will definitely sit a level deeper in the team so they’ll be more often than not interfacing with data scientists and possibly product managers.
reference – Data Scientist vs. Machine Learning Engineer
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