Difference Between a Data Scientist and a Machine Learning Engineer

The opportunities in Data Science and Machine Learning are growing exponentially and in this article, we talk about the difference between two important roles in this field which is Data Scientist and Machine Learning Engineer and we will evaluate it on three fronts which are Job Duties, the skill set and the Salaries. So let’s start.

Differentiate Based on Job Duties:

The role of a Data Scientist is to follow the complete data science process or data science pipeline which is understanding a business problem, collecting data, doing exploratory data analysis, building a model, and drawing the insights. This is the duty of a data scientist. 

They follow all these steps and then they represent these insights to the stakeholders. So their job involves a lot of creativity, domain knowledge, understanding business problems, data cleaning techniques, and then of course, machine learning, model building, and then generating insights. But often data scientists build these models in their environment, and when they have to deploy this to production, they take help of machine learning engineers.

Now Machine Learning Engineers are responsible for maintaining and creating the machine learning infrastructure so that they can deploy the models built by data scientists to production and not only they can deploy but to scale them. So when it comes to enterprise solutions, you need to think about scaling the system using distributed computing so that you can solve the needs of clients in a time-efficient manner. All of this activity is done by a machine learning engineer. 

you can see that data scientists are kind of the users of machine learning engineers and when I say deploying it to production it could be deploying it to wearable devices. You might have a wearable device which is tracking your heartbeats and uses some machine learning model for doing some prediction. So how do you deploy that model on a little wearable device that has less memory and computation resources so that it performs fast? 

Think about autonomous cars. So Tesla cars can detect objects and it can do autonomous driving. So that car has little computer sensors and those are running those models. So how do you efficiently run those models? How do you take care of errors? All of those things are the responsibilities of a machine learning engineer. Think about technical skills, data scientist and machine learning engineer roles are quite overlapping.

Many times, if you go to small companies, machine learning engineers might be doing the work of data scientists and vice versa. So there is a lot of overlap.

Differentiate Based on Skill Set:

There are a few common skills, such as knowing about machine learning, predictive modeling, statistics and math, and Python. These are common skills that both data scientists and machine learning engineers have. 

On top of that, our data scientists are good in terms of telling the data stories, creativity, and understanding business problems. So many times you will see data scientists have a strong domain knowledge. For example if you are in finance a person might be a CFA you know he might have a strong finance background and then there that person will learn technical background and become a data scientist. Machine learning engineers on the other hand are core They understand computer science. 

It’s kind of hard to become a machine learning engineer without having a computer science background. They understand memory, CPU utilization, distributed computing, fault tolerance, and log analysis. They know all of those cool computer science techniques. programming language data scientists are good in either Python or R whereas machine learning engineers are good in C++, and Java A job is like a casino. 

Nowadays, if you have a job in C++, Java, Python, Scala, etc. Often machine learning engineers build their algorithms know when they write let’s say they are not happy with the default implementation of a decision tree from a scale they might come up with their implementation with little twigs and those implementations they will generally do it in either Java or C++ they want the efficiency they at a little upper level where they will use the the modules or the libraries created by machine learning engineers to solve those problems. 

Data scientists often use BI tools such as Power BI Tableau for doing data analysis. Many times data scientists don’t use ML at all. They can do data science using Excel or Python pandas and Power BI. Whereas machine learning engineers’ core role is to do machine learning. So they will be doing machine learning, and deep learning all the time. For data scientists, machine learning is one of the tools for doing data science. 

Differentiate Based on Salary:

Data Scientists and Machine learning engineers both are some of the highest-paid professionals in the world. According to Glassdoor.com, If you become a Data scientist and Machine learning engineer, the first step you take is Data scientist or Data engineer, which is the first level, in which you can take a package of 6-8 lakhs per annum. 

Then we talk about the next level, after taking 2-3 years of experience, you can apply for Senior Data scientist or Data engineer, in which your income or salary package is between 9-14 lakh per annum. 

Then, after 2-3 years, you reach the Data scientist or Data engineer, level 3 where you expect a salary of 14-22 lakh per annum. 

Then, after gaining some more experience, you can take the role of principal Data scientist or Data engineer, whose salary is 22-37 lakh per annum and the last one is director of Data which you expect a salary of up to 50 lakh per annum. Beyond this, there are fields where you gain knowledge and handle the team. 1CR plus packages are also available in the job role of Data scientist or Data engineer.

Conclusion: 

So I hope that clarifies the difference between a data scientist and a machine learning engineer. Based on your skill set and interest level, you can choose to pursue one or the other career. And of course, if you’re a data scientist, you can switch to ML, or an ML person can switch to a data scientist so you always have that flexibility.

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