Hello Readers, Today we inform you of the Differences between, Data Scientists and ML Engineers. If you like this information, please share it with your friends. Leave me a comment to improve my writing skills and subscribe by email for future updates.
There’s often confusion between the roles of Data Scientists and Machine Learning Engineers. Although they certainly work together amicably and enjoy some overlap concerning expertise and experience, the two roles serve quite different purposes. Essentially, we are differentiating between Scientists who seek to understand the science behind their work, and Engineers who seek to build something that can be accessed by others.
To Clear Discussion I'll make Three Categories;
- Expertise
- Responsibilities
- Salary Expectations
Expertise
The reason people get confused about the difference between the two roles is that there are many places where their skills overlap. For example, both Data Scientists and Machine Learning engineers are expected to have good knowledge of,
- Supervised & Unsupervised Learning.
- Machine Learning & Predictive Modelling.
- Mathematics and Statistics.
- Python or R.
The major overlaps between the roles have resulted in some organizations, particularly smaller organizations and startups, merging the roles into one. Thus, some organizations have Data Scientists doing the work of Machine Learning engineers and some have Machine Learning engineers doing the work of Data Scientists. Only leading to more confusion amongst practitioners. However, there are some key differences between the expertise required for each role.
Data Scientists are typically extremely good data storytellers. Some would argue that this trait makes them much more creative than Machine Learning engineers. Another difference is that Data Scientists may use tools like PowerBI and Tableau to share insights to the business, and they don't necessarily need to use Machine Learning.
Machine Learning engineers are expected to know about Data Structures & Algorithms and understand the fundamental components that go into creating deliverable software. And it's not unusual for a Machine Learning engineer to have a good grasp of another programming language like Java, C++, or Julia.
Responsibilities
Data Scientists follow the Data Science Process, which may also be referred to as Blitzstein & Pfister workflow. Blitzstein and Pfister initially created the framework to teach students of the Harvard CS 109 course how to approach Data Science problems.
The Data Science process has 5 key phrases, are following
- Understanding the Business Problem
- Data Collection
- Data Cleaning & Exploration
- Model Building
- Communicate and Visualize Insights
The majority of the work performed by Data Scientists is in the research environment. In this environment, Data Scientists perform tasks to better understand the data so they can build models that will best capture the data’s inherent patterns. Once they’ve built a model, the next step is to evaluate whether it meets the project's desired outcome. If it does not, they will iteratively repeat the process until the model meets the desired outcome before handing it over to the Machine Learning Engineers.
Machine Learning Engineers are responsible for creating and maintaining the Machine Learning infrastructure that permits them to deploy the models built by Data Scientists to a production environment. Therefore, Machine Learning Engineers typically work in the development environment which is where they are concerned with reproducing the machine learning pipeline built by Data Scientists in the research environment. And, they work in the production environment which is where the model is made accessible to other software systems or clients.
Machine Learning engineers are responsible for the maintenance of the ML infrastructure that allows them to deploy and scale the models built by the Data Scientists. And, Data Scientists are users of the Machine Learning infrastructure that is built by the Machine Learning engineer.
Salary Expectations
It is difficult to lower the expectations of an accurate salary. Salaries in both roles vary based on various factors such as the amount of experience you have, the qualifications you have, the position you are in, and the field in which you work.
Institutions are expected to offer different benefits. Whatever the role, you can expect to receive invitations to join a company pension plan, flexible or remote work, performance bonuses, and private medical insurance.
India
- A Junior Data Scientists can expect to start within the range of ₹500,000 - ₹650,000 per year (It may rise to ₹1,000,000 per year depending on experience). A graduate or Entry-level Machine Learning Engineer can expect a starting salary of ₹450,000 - ₹575,000 per year (It may rise to ₹1,700,000 per year depending on experience). [Source: payscale]
- According to research, the average salary of a Data Scientists in the India is ₹10,00,000 per year. The prospectus states that the average salary of a Machine Learning Engineer in the India is ₹701,478 per year.
- Lead and Chief Data Scientists can earn anything upwards of ₹1,000,000 (surpassing ₹2,000,000 in some cases). In contrast, More experienced Machine Learning Engineers can expect to earn as much as ₹1,000,000 (particularly if they work for a multinational company like TCS or Intel or etc.).
United States of America
The average base salary of a Data Scientists in the USA is $120,089 per year. In contrast, the average base salary of a Machine Learning Engineer in the USA is $150,660 per year. [Source: Google].
Overall, it is Fair to say that machine learning engineers are generally paid more than data scientists throughout the year.
Final Thoughts From Me
Despite the similarities between the roles, Data Scientists and Machine Learning Engineers are quite different concerning their responsibilities, expertise, and earnings. From the majority of interviews I’ve listened to on the topic, many say the transition from Data Scientists to Machine Learning Engineer is much harder than the transition from Machine Learning Engineer to Data Scientists. This is because Data Scientists aren’t usually proficient with software engineering and computer science fundamentals which is a big learning curve.
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