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Machine Learning Engineer and Data Scientist are two of the Hottest Jobs in the Industry right now and for good reason. With 2.5 Quintillion bytes of data being generated every day, a professional who can organize this humongous data to provide business solutions is indeed the hero! The competition between Machine Learning Engineer vs Data Scientist is increasing and the line between them diminishing.
The mix of personality traits, experience, and analytic skills required for this is considered difficult to find, and, thus, the demand for qualified Data scientists and Machine Learning Engineers has exceeded supply in recent years. So, let’s begin the “Machine Learning Engineer vs Data Scientist” article to find out the differences between the two Professionals in the following order:
Although there are several definitions of Data Scientists available, basically they are professionals who practice the art of Data Science. Data Scientists crack complex data problems with their expertise in scientific disciplines. It is a position of Specialists.
They specialize in different types of skills like speech, text analytics (NLP), image and video processing, medicine and material simulation, etc. Each of these specialist roles is very limited in number and hence the value of such a specialist is immense. Since we are comparing Machine Learning Engineer vs Data Scientist, Let’s see who is an ML Engineer.
Machine learning engineers are sophisticated programmers who develop machines and systems that can learn and apply knowledge without specific direction.
Artificial intelligence is the goal of a machine learning engineer. They are computer programmers, but their focus goes beyond specifically programming machines to perform specific tasks. They create programs that will enable machines to take actions without being specifically directed to perform those tasks.
A lot of Job posting for Data Scientists emerged and flooded the market during 2012. The same is happening for the Machine Learning Engineer Role, it’s a relatively new one and is slowly emerging at places where we have Data Specialists. The terms are nebulous because they are new. Now, if we compare Machine Learning Engineer vs Data Scientist, there are a few parameters that we need to consider:
The Average Salary of Data Scientists is around $91,470 (US) or ₹693,637 (IND). Let’s have a look at the Salary of a Data Scientist according to the Experience.
|Entry Level – IND||₹306,054 – ₹1,215,966|
|Entry Level – US||$60,894 – $127,894|
|Experienced – IND||₹972,106 – ₹2,928,194|
|Experienced – US||$79,321 – $167,947|
This figure also depends upon a few other factors like the Company one is working for or the Location. But majorly the above table depicts the average salary range for the different level of experience.
Now, the Average Salary of a Machine Learning Engineer is around $111,490 (US) or ₹719,646 (IND). Let’s see the Salary Compensation of a Machine Learning Engineer.
|Salary||$76,953 – $151,779|
|Bonus||$2,974 – $25,541|
|Profit Sharing||$1,934 – $51,285|
|Total Pay||$76,184 – $162,727|
So, if we compare the Salary Trends of Machine Learning Engineer and Data Scientist we can see that in general, a Machine Learning Engineer Earns a little more than a Data Scientist. Now one might ask why is that, so for that, we need to have a look at the skills and the differences in roles between Machine Learning Engineer vs Data Scientist. But first, let’s have a look at the Job Trends.
Data Scientist Job Trends
|Location||No. of Jobs|
|New York, NY||1189|
|San Francisco, CA||1107|
Machine Learning Engineer Job Trends
|Location||No. of Jobs|
|New York, NY||1813|
|San Francisco, CA||1487|
On one hand, Machine Learning Engineers get slightly more paid than Data Scientist, on the other hand, the demand or the Job openings for a Data Scientist is more than that of an ML Engineer. This is because ML Engineers work on Artificial Intelligence, which is comparatively a new domain.
Now the skill requirements for Machine Learning Engineer vs Data Scientist are very similar, so let’s start with the Common Skillsets.
Although Python is a very good Language, it alone cannot help you. You will probably have to learn all these languages like C++, R, Python, Java and also work on MapReduce at some point.
If your findings can’t be easily and quickly identified, then you’re going to have a difficult time getting through to others. For this reason, data visualization can have a make-or-break effect when it comes to the impact of your data.
Deep Learning has taken traditional Machine Learning approaches to a next level. It is inspired by biological Neurons (Brain Cells). The idea here is to mimic the human brain. A large network of such Artificial Neurons is used, this is known as Deep Neural Networks.
Therefore, we require frameworks like Hadoop and Spark to handle Big Data. Nowadays, most organizations are using Big Data analytics to gain hidden business insights. It is, therefore, a must-have skill for a Data Scientist and Machine Learning Engineers.
You won’t be able to discern the problems and potential challenges that need solving for the business to sustain and grow. You won’t really be able to help your organization explore new business opportunities.
So it’s necessary to have good control over libraries like Gensim, NLTK, and techniques like word2vec, sentimental analysis, and summarization.
Knowledge of Time-frequency Analysis and Advanced Signal Processing Algorithms such as Wavelets, Shearlets, Curvelets, and Bandlets will help you to solve complex situations.
Communication is going to make all of this much easier. Companies searching for a strong Data Scientist are looking for someone who can clearly and fluently translate their technical findings to a non-technical team, such as the Marketing or Sales departments.
Now we come to the final chapter of Machine Learning Engineer vs Data Scientist, ie. what exactly they do in their day to day life and what challenges they face.
Machine Learning Engineer Roles:
Data Scientist Roles:
Machine Learning Engineer
Now, with this, we come to the end of this Machine Learning Engineer vs Data Scientist Article. I hope you got an In-Depth understanding of the two professionals and how they differ in terms of Skillsets, Roles, and Salary.
Edureka’s Python for Data Science Course help you master important Python programming concepts such as data operations, file operations, object-oriented programming and various Python libraries such as Pandas, Numpy, Matplotlib which are essential for Data Science.
The Machine Learning Certification Training using Python helps you gain expertise in various machine learning algorithms such as regression, clustering, decision trees, random forest, Naïve Bayes and Q-Learning. It also exposes you to concepts of Statistics, Time Series and different classes of machine learning algorithms like supervised, unsupervised and reinforcement algorithms.
Got a question for us? Please mention it in the comments section of the “Machine Learning Engineer vs Data Scientist” article and we will get back to you.