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Often confused between the difference between a machine learning engineer and a data scientist? Then you have arrived at the right destination. We are here to decipher the topic of machine learning engineer v/s data scientist.
Let us start by acknowledging the fact that machine learning lies under the large umbrella of data science.
It should also be noted that certainly a machine learning engineer and a data scientist must work together in harmony.
Also, there is an overlapping of skills and experiences. Although there is an overlap of skills and experiences both the roles are essentially different.
Summing up all the points that we just told in simple words we can say that certainly a data scientist and a machine learning engineer work together in an amicable manner, skills required for both the roles also overlap to some extent.
However, both roles serve distinct purposes. Let us try to find out what are they.
Check out this article till the end in order to know about machine learning engineer v/s data scientist salary in India, the difference between machine learning engineers and data scientists and more.
Machine Learning Engineer vs Data Scientist
So in this article, we are going to find the differences between a machine learning engineer and a data scientist.
So let us see the differences between a scientist seeking to understand the science behind their work and an engineer trying to build something that people can access.
However, you should understand that both these roles are equally important for a company/ an organization. If needed the roles of machine learning engineer and data scientist can also be interchanged.
In the below sections let us try to see how machine learning engineer and data scientist roles differ depending upon their responsibility, salary, and expertise. Also, check out the best machine learning course in Kerala.
Responsibilities of a Machine Learning Engineer vs Data Scientist
Let us try to see the key responsibilities of a data scientist, they include
- Understanding / evaluating the problem
- Collecting data
- Exploration and cleaning of data
- Building a model
- Visualization of insights
Data scientist major portion of their tasks in the research environment. In the research environment they can perform better, by understanding the data, performing model building and then capture the data pattern.
After building a proper data model they will decide if this model will meet the project outcome.
If the model does not meet the project outcome process will be repeated to meet the desired output. The model will be handed over to the machine learning engineers only after they get a favorable output.
Machine learning engineers create and maintain the machine learning environment in order to deploy the models of the data scientist to production.
Thus a machine learning engineer works in a development environment. Hence for the machine learning pipeline that the data scientist has built is extremely important. Machine learning engineers make the model accessible to clients or software systems.
To conclude machine learning engineers deploy the models created by data scientists.
Machine Learning Engineer vs Data Scientist Salary
Be a machine learning engineer or a data scientist pay scales for both roles is dependent on various factors. Those factors include qualification, experience, the location you have chosen, etc.
Depending in the organization you are working for and regardless of your role, you may also be eligible for company perks. The perks include a pension scheme, remote working, bonuses, medical insurance, etc.
- The salary expectation for a junior data scientist can be anywhere between $25000 – $35000.
- Whereas for a junior machine learning engineer it can be $35000 and $40000
- For experienced data scientists the salary can go upto $60000 and for machine learning engineers it could go upto $170000
From the salaries said above we can notice that the pay scale for machine learning engineers is higher than data scientists.
Now let us try to see where the skill sets of machine learning engineers and data scientists overlap. Be it machine language engineers or data scientists candidates belonging to either of the roles are expected to have knowledge in the following fields
- Statistics/ mathematics
- Machine learning
- Python or R
- Predictive modeling
- Supervised and non-supervision learning
In small organizations or start-ups both the roles are merged into a single role.
Also in certain other organizations, data scientists and machine learning engineers interchange their roles too.
Let us try seeing the key difference in the expertise of machine learning engineers and data scientists
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Differences in Expertise
- Data scientists are essentially data storytellers, this trait arguably makes them more creative.
- Also, data scientists uses tools such as powerBI and tableau and don’t have to use machine learning
- Whereas machine learning engineers have a solid foundation in software engineering and computer science. ML engineers know the essential component that is needed for delivering a software such as algorithm and data structures
Its a wrap
We hope this article on machine learning engineer v/s data scientist was helpful. Key takeaways from this article is that although there is overlapping of skills to a certain extent both the roles are different depending on their expertise and responsibilities. For more such articles on data science and machine learning subscribe and stay tuned to entri.
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