Machine Learning Engineer Vs Data Scientist
By Kamal Jacob
In the past decade, words such as “Artificial Intelligence”, “Big Data”, “Machine Learning” have become so prominent. In fact, the job roles of Machine Learning Engineer and Data Scientist is one of the most hottest trending jobs in the industry. In a recent study by Glassdoor, the job role of data scientist was considered the top job in the United States. Another study conducted by the Analytics India Magazine also revealed that Machine Learning was the next job role picking up pace with the role of a data scientist. With zillions of data being generated by companies, the need for someone who can organize these data and manipulate it to deliver business solutions is on the rise. IDC estimates that AI and ML spending will grow to almost $57.6B in 2021.
While these technologies are definitely in the limelight, there are lots of confusions in the minds of people as to which of these to choose as a career. The primary reason is that it is relatively a new field and the popularity of these technologies in the recent years which has opened a lot of opportunities across the different business segments. In this article, we will address the differences between a Machine Learning Engineer and a Data Scientist. Before getting into the topic, let’s understand the difference between Machine Learning and Data Science.
What is Machine Learning and Data Science?
Machine Learning is an application of AI that gives computers the ability to learn without being programmed. With ML, computers are trained by building the machine learning models by using the data that is available and different algorithms. These algorithms make software applications to be able to predict the outcome without any programming. Example of machine learning are the “Recommended for You” section on Amazon that displays personalized product recommendations according to your shopping behaviour.
Data Science is about getting deep into the data and understanding the patterns, trends and behaviours. In simple terms, data science is about predictions and inferences from the available data. These valuable insights help businesses understand the user behaviour and interest and make better informed business decisions. For example, Netflix identifies the user viewing patterns to understand what they prefer and uses the information to release new series on their website.
Who is a Machine Learning Engineer and Data Scientist?
Now that we have a basic understanding of Machine Learning and Data Science, let’s understand who is a Machine Learning Engineer and Data Scientist –
Data Scientist – Data scientists are hired by organizations as they help businesses to extract valuable insights from the data. Data scientists analyse the data and develop programs using programming languages (e.g., Java) that can find out patterns in the data. With this data, businesses can learn more about the user behaviour, engagement rates, and more. Data Scientists focus mainly on the research to determine the type of machine learning approach, model the algorithm and then prototype it for testing purposes.
Machine Learning Engineer – ML Engineers are similar to other engineers who are into development activities, specifically into machine learning. They build algorithms on top of the data models that are defined by data scientists. In addition, ML engineers also have the knowledge to develop programs that can control computers. With the help of algorithms developed by ML engineers, the machines can understand commands without someone having to teach them.
According to the study conducted by Analytics India Magazine, there are more than 78000 jobs available for the role of data scientists and machine learning engineers in India. The numbers are only growing based on the demand. In the US, a search on Glassdoor for “data scientist” jobs yields more than 26000 job results. Similarly, LinkedIn lists more than 6000+ ML jobs in India and Indeed lists about 17470 available jobs for ML engineers in the United States.
Both the roles of data scientist and machine learning engineer require a common set of skill sets as well as specific skill sets. Let’s list down the common skill sets for both of these job roles –
Common Skill Sets
a) Strong understanding of the industry so that the solutions can be focussed towards solving real problems and pain points. The ultimate thinking of people in these job roles should be focussed towards the success of the organization.
b) C++, Java, R, Python programming languages
c) Ability to visualize large amounts of data and perform data-mining to extract valuable information that can be useful which can help businesses to keep their customers engaged.
d) Good understanding of frameworks that can process and handle Big Data like Hadoop
e) Basic understanding of machine learning, neural network architectures, deep learning, computer vision concepts
Next, let’s take a look at what the Machine Learning Engineer and Data Scientist do in their daily life –
a) The primary role of a data scientist is to understand the customer business need and come up with a solution
b) Perform data mining to see if they can extract valuable information that could benefit companies
c) Identify opportunities for improving the process in the organization that generate better results
d) Make use of Deep Learning Frameworks like TensorFlow to build the Deep Learning models
e) Data analysis using different methods and representing the data using graphs, charts and so on
Machine Learning Engineers
1. ML engineers perform the research and implement appropriate ML algorithms and tools
2. Develop business models that will help understand the business objectives
3. Develop machine learning applications depending on the business requirements
4. Experiment with existing ML frameworks and libraries and extend them, if necessary
5. Verify the quality of data
Job Role Requirements
Job Role Requirements of a Data Scientist
To start a career as a data scientist, you must be highly educated such as a Master’s Degree or even a Ph.D. degree. Reports show that about 16 percent of data scientists have an advanced degree in engineering, 19 percent in computer science and 32 percent in mathematics and statistics. In addition to degree, you also must have –
1. Strong experience in building the models and manipulating data sets
2. Strong experience working with C++, Java, SQL and Python programming
3. Experience with data-mining and machine learning techniques
4. Hands-on experience using web services
5. Creative thinking with the ability to look at the numbers and the trends and make decisions.
6. Very good communication skills
Job Role Requirements of a Machine Learning Engineer
For a career as a machine learning engineer, you can start off if you have a Master’s degree in Computer Science. However, given the plethora of opportunities in the market, companies are ready to take you in if you have the knowledge and the right experience. The basic required experience for a machine learning engineer are –
1. Just like the data scientist, you must have experience with programming languages like C, C++, Python, R, Java and so on
2. Strong mathematical skills and excellent knowledge of probability and statistics concepts
3. Experience with distributed systems and messaging tools (RabbitMQ, Kafka etc)
4. Very good communication skills
The salary drawn by a Data Scientist and machine engineer can vary depending on the nature of their job role and the country/location where they are located.
According to a survey by Indeed, the average salary of a data scientist is $121,018 per year (in the United States). Glassdoor reports the average salary of a data scientist of $110,000 per year.
The below image shows the average data scientist salary for India and the US. Reference: Edureka (originally from Payscale.com).
Machine Learning Engineer
According to a survey by Indeed, the average salary of a data scientist is $140,470 per year (in the United States).
Below tables show the salary compensation for positions in the US and India. Reference: Edureka
As you would have probably understood by now there are a lot of areas that are common to both the careers. It depends on what your interest areas are and how you want to shape your career. Be it either of these, you will be working on the technology that has a great demand for talent even in the forthcoming years. Therefore, you can safely wipe out any doubts about choosing a career in either of these fields.
If you’re ready to embark on your journey as a Machine Learning engineer or a data scientist, the first step is to get yourself enrolled in an accredited learning program. Manipal ProLearn’s Post Graduate Certificate Program in Data Science and Machine Learning partnered with Gramener and Equifax covers everything you need to become a skilled, qualified and competent data scientist.