Two Data Science Jobs
There are several data science jobs. For anyone looking to build a career in this field, it is a complicated task to determine which path to choose out of the plethora of options. Sometimes, the paths may not be completely different. Data science jobs often come with overlapping skill requirements. In this blog, I provide an overview of what separates data scientists from data engineers, and what responsibilities unite them at work.
Who Is a Data Scientist?
A data scientist is someone, who is generally good at mathematics and statistics. His proficiency in these subjects makes him a natural analyst with a penchant for machine learning and artificial intelligence modelling. Their work requires programming because advanced applied analytics would not be possible without it. Quite a few data scientists are mathematicians and statisticians, who reluctantly picked up programming for this very reason.
Free Step-by-step Guide To Become A Data Scientist
Subscribe and get this detailed guide absolutely FREE
A data scientist is also someone, who has a good understanding of the domain he is operating in. He can use this to acquire business intelligence and help his organization go further along the path of success. To this end, he is not only an analyst, but also a verbal and visual communicator of insights from data.
Who Is a Data Engineer?
A data engineer is most likely a programmer from the past. He is proficient at Python, Java or Scala. He is adept at handling distributed systems for the analysis of voluminous big data.
His primary responsibility is creating free-flowing data pipelines. This, however, is not an easy endeavor. It requires the data engineer to combine numerous big data technologies. With advancements in these technologies, he is increasingly expected to create data pipelines that enable real-time analytics – analysis live data.
It is important to note that the data engineer is different from the DevOps professional or the systems administrator. He is not responsible for the operation of all computing systems in an organization but only for those parts of systems that are relevant for data pipelines and analytics.
Safe to say that the overlapping parts of these two roles involve big data. The data scientist and data engineer should technically work together to achieve the organization’s goal(s). Even if their contributions may and should differ.
From what we have discussed so far, it is clear that the data scientist shares his skill in programming with the data engineer. Although the latter is likely to have a far more superior grasp of this skill. The data scientist will in turn be superior at data analytics. The data engineer will most likely only have basic analytical skills that help him understand the requirements of his projects – the requirements of his systems and the deliverables of his pipelines.
The two roles are complementary. They are not interchangeable as both sets of parties have their strengths and weaknesses. When they perform the role that suits their strengths, the team is likely to be more efficient. Hence, it is important that the data scientist be responsible for asking the right business questions and looking for their solutions through proper data analysis. It is also important that the data engineer be less responsible for these aspects and more for what he is primed to do – create and manage data pipelines.
Problem of Mixing Roles
If an organization does not draw distinct lines between these two data science jobs, it could have serious consequences for their productivity. Trying to make a data scientist fulfil the role of a data engineer is inefficient to say the least. It will also most likely frustrate him and make him leave the job eventually. The same can be said of the data engineer, who is made responsible for the tasks of a data scientist.
A good indicator of whether either professional is being made to do the other’s job is the data scientist-data engineer ration. Technically, an organization should have more data engineers 2-3 (in bigger projects and companies 4-5) for every data scientist. The task of creating and maintaining data pipelines is more heavy-duty than the task of using machine learning and artificial intelligence for data analytics.
If there are more data scientists than data engineers, it most likely indicates that the organization is using some of their data scientists to fulfil data engineering responsibilities. Conversely, if there are 8-9 data engineers for every data scientist in the organization, then it is highly probable that the data engineers are also conducting data analysis with their inferior knowledge of machine learning and artificial intelligence.
Birth of Machine Learning Engineers
The two roles that we are different and yet similar. The overlapping skill sets of these professionals make them possible candidates to replace each other, if they can learn the skills that they are lacking. While some argue that it is easier for a data engineer to become a data scientist because they only need to master mathematics and statistics, and machine learning and artificial intelligence (which is a considerable lot), this is not so. Clearly, both professionals need to do quite a bit if they are to be any good at the other’s job let alone replace them. But it is possible.
The world we live in offers plenty of opportunities to grow. It is competitive, and individuals are motivated than ever before to reap the benefits of what they sow. Data scientists and data engineers are competent professionals. They realize the value on offer if they can learn and grow. Moreover, there are programs to help them make up for their shortcomings. This situation has created a new kind of data science professional – the machine learning engineer.
Who Is a Machine Learning Engineer?
The machine learning engineer is quite simply an amalgamation of the data scientist and data engineer. He is a jack of both these trades, as well as, a professional at them. The main objective of a machine learning engineer is to operate in the grey areas of machine learning and artificial modelling that are prone to uncertainty with the precision and discipline of perfect engineering.
Essentially, they focus on optimizing programs and systems for machine learning. Although, this makes them seem like a superior data scientist or data engineer, it is not (yet) so. The machine learning engineer will still naturally be more adept at one set of skills (either that of the data scientist or the data engineer) than the other because of their background. They are still mostly either data scientists or data engineers with more experience and knowledge of the other’s skills now.