Hiring a Data Scientist, Engineer or Analyst: Find the Right Person for your Business
“Data is the new oil!” That is a famous phrase these days. But, unfortunately, it is a flawed and narrow way to look at data since, unlike finite oil, data is reusable. The better analogy for data would be water, which is essential for life and constantly being recycled. The use of data has exploded in the last decade and will only continue to grow. So what does this mean for businesses? It means that you need someone on your team who can help you make sense of all of this data and turn it into insights that will help you make decisions about your business.
In this blog post, we will discuss what each one does and which one is best suited for your business. We’ll also talk about how to find the right person so that you can start growing your company!
In reality, everyone nowadays should have a basic understanding of data. However, when looking at a job description for a data analyst, scientist, or engineer, these terms are almost always used interchangeably. So, what are the distinctions between these three individuals, and which of the three will add the most value to your company?
The most critical distinction between these three data roles, in my opinion, is the level of responsibility. Let us take a closer look at what exactly these roles are.
Data Scientists
The title “data scientist” is the most recent and vague of the three. A data scientist is someone who has a mix of skills in statistics, computer science, and domain expertise. They are able to take raw data and turn it into insights that can help inform business decisions. Data scientists are typically employed by large companies or startups that can afford to hire an individual with these unique skill sets.
Data scientists are often creative, wondering what else they can do with the data.
Data Engineers
An engineer is someone who knows how data flows through a system and is able to create, manipulate, or improve existing systems that are built around collecting and analyzing data. Engineers are typically employed by large companies since they require the most technical know-how in order for them to be successful. The distinction between a data scientist and a data engineer is subtle. One could argue that no single authority can come forward and emphasize the distinction. Data engineers are fervent machine learning, natural language processing, and artificial intelligence experts who pioneer new projects.
Is it necessary for engineers to have a working knowledge of SQL? While this is a benefit, they will not use this as frequently as an analyst would.
For the most part, businesses have no idea what data engineers are doing. They only need the data engineer to come up with something amazing.
They have a licence to chill or Kill
Data Analysts
An analyst is someone who has a strong business background and knows how to use data to answer specific business questions. They are able to take data and turn it into insights, but they may not have the technical skills that a data scientist or engineer possesses. An analyst is typically employed by companies of all sizes, but especially small businesses.
A strong understanding of SQL is essential for a good analyst, especially if the company’s data is stored in a structured database.
Analysts thrive best in an office setting (the 9 a.m. to 5 a.m. routine), solving problems that have been assigned to them.
It’s important to note that the line between an advanced data analyst and a data scientist is thin.
In programming jargon, Data Analysts are similar to software quality testers who sort through and evaluate whether what the Scientists and Engineers have created actually meets the intended purpose. However, as a tester, you must be aware of the underlying rules and principles.
Titles don't mean much these days.
The title of your position will typically determine which type of person you need to hire. For small businesses, it is likely that an analyst will be able to help add the most value since they are more familiar with business acumen and know-how to use data in order to answer questions related specifically to their company or industry. This may not always be the case, so it is important to understand the specific needs of your business in order to make the best decision.
All of these roles share the requirement of having a working knowledge of statistics. Here’s the thing: it’s not unheard of for a data engineer in one company to do what a data scientist in another does, and vice versa.
Remove the word data from these titles, and you’ll have a better chance of understanding what each role brings to the table. These days, titles are not always good descriptors.
When hiring, a company should be specific about what they expect and not rely heavily on titles. And, in terms of hiring, a good degree isn’t worth much these days. Employers can’t tell what you know just by looking at your bachelor’s degree. A person with professional certification may appear to be technically adept.
Hiring someone with a Power BI, Tableau, or Python Certification is preferable to hiring someone with a Masters Degree in Information Technology. This is due to the fact that degrees vary greatly; no two degrees from two different universities are the same.
The key takeaway here is that before filling any of these roles, the company should be clear about what it wants. If it does not, it runs the risk of hiring a superb analyst, scientist, or engineer who does not understand what the organization wants, and everyone loses in the end.
Do not put the cart in front of the horse; the horse comes first, followed by the cart.
No matter which type of individual you decide to hire, it is important that they have the proper skills and background in order for them to be successful. The best way to find the right person for the job is by doing your research and making sure that their skill set matches what you are looking for. You can also ask questions during an interview to determine whether or not they are the right fit for your company.
In the end, it is important to remember that data is constantly evolving and changing, so you will likely need to adapt your team as your business grows. The right person for the job today may not be the right person for the job tomorrow, so it’s important to be flexible and willing to make changes when necessary.
