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How to Distinguish a Real Data Scientist from a Faker?

Andrew Zola
I have many passions, but the main one is writing – learning about new things and connecting with diverse
audiences is something that has always amazed and excited me.
Andrew Zola on Linkedin

Saying that there’s a huge demand for data scientists is an understatement. In fact, the high demand for actionable analytics and immediate business intelligence has sent salaries soaring.

Today, a data scientist earns about $113,436 on average and these types of wages have attracted many who want to get a piece of the pie. Unfortunately, this also means that there will be a few posers trying to make as much as they can before being found out.

Sometimes, this is not the result of a desire for deception, instead, it can also be the consequence of a widely misunderstood job description that has people who purely deal with data think that they’re data scientists.

When people don’t have the necessary skill set, they end up building ineffective data models. Data modeling is definitely not just another tool, so it isn’t something that can be learned quickly or easily.

While some of these fake data scientists might mean well, they can badly hurt your business. So it’s important to spot faker as quickly as possible.

So what’s the best way to go about this? Let’s dive right into it.

Fakers don’t possess a highly quantitative advanced degree

What quickly gives them away is the lack of an advanced degree within a highly quantitative space. While there have been the odd one or two exceptions, it’s really rare for someone to have the necessary technical skills without an advanced degree.

Many in the field will agree that without a strong foundation in a technically rigorous program, it’s almost impossible to master all the required computer science and statistical concepts that are needed to be an effective data scientist.

Their professional network lacks other data scientists

When you check out LinkedIn profiles, you will notice that one’s professional network is filled with other individuals within the same field. If you don’t believe us, go take a look at your own LinkedIn profile!

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If the candidate you’re interested in hiring lacks other data scientists in their network, then it can work as a red flag. If they don’t know others within the same industry, that’s probably a good chance that they’re not really data scientists.

They haven’t worked with “R”

“R” is a programming language that every data scientist should know inside out. If they haven’t worked with R or any other similar tools before, you can bet that they’re not cut out for the job.

They come from a purely academic or research background

While those coming from a stellar academic or research background might work well within a corporate environment, they might not have the necessary corporate experience to be a good fit.

The key to being an effective data scientist is to have the business acumen to understand how findings can impact business goals. They also have to know how to deliver actionable insights to business leaders in an easily digestible manner.

However, an individual with a strong academic background can be much closer to a data scientist than others. They just lack corporate experience.

This can sometimes be remedied easily with appropriate training and adequate time to gain relevant experience.

They lack concrete examples of experience with statistical analysis or unstructured data

Just listing tools like AWS, Python, or Hadoop is meaningless unless candidates can support it with projects evidence (and a list of tools that were put to good use). If they’re unable to provide clear examples of their experience working with unstructured data or if examples of their involvement are vague, they’re probably not really good data scientists.

The same is true if the person has experience organizing and structuring large data sets but hardly any experience with analytics or statistical concepts. In this scenario, they’re more likely to be a data engineer, not data a scientist.