New Blood of Data Scientists comes from Academia

New Blood of Data Scientists comes from Academia

There are about 3 million data scientists globally and exactly the same amount of job postings every year for data scientists. However, on average, data scientists change jobs every 2 years and not every year, so 50% of these positions are left unfilled. Additionally, the demand for artificial intelligence and machine learning specialists has been growing by 30% every year; this is in line with the 40% R&D growth in this sector.

Nevertheless, there is evidently a massive gap in the demand and supply of data scientists, and it is no surprise that the position was named one of the hottest jobs in the last few years. At Smart Tribe, we unlock thousands of the most qualified and talented data scientists in the world coming from all scientific disciplines and are able to fill that gap in the demand for bright experts in artificial intelligence.

Not all Data Scientists have to be Computer Scientists

“Companies find it really hard to find data scientists” agrees Kris Jack, Smart Tribe co-founder who is also an AI specialist with a PhD in computer science. “They often go for the traditional route of being able to recognise that someone is a data scientist because they had the role of being a scientist before. There are not enough people who have been data scientists before to fill those roles, so they need to get good at recognising who can be good at being a data scientist who hasn’t been before. There is this tendency to go for people who have worked in computer science or software developers who worked in quantitative analytics roles because they’re quite close to data science jobs.”

What we commonly see when we look at the professionals who have been hired for these positions and do the best job are those coming from academia. However, it can be a very difficult choice to make when you’re hiring someone who hasn’t performed the role before and who looks like they’re not a data scientist. 

“They look like they’re physicists.They look like they’re rocket scientists. They look like mathematicians. It is hard sometimes to get over that psychological barrier of saying that you could, you are not just physicist. You’re not just a rocket scientist, but actually you can do the job of data scientist because they understand the scientific method – they are quick learners. They are able to program because so many of the hard sciences require programming skills nowadays, and they know how to work with big data”, adds Kris.

The VERY Big Data does not even come from Computer Science

Academics across many scientific disciplines, such as geneticists and physicists, are working on real big data. For example, scientists working in CERN have much bigger datasets when compared to Internet companies, yet they do not call themselves data scientists. 

They have all the necessary skills, but they often describe them in a not-so-obvious way if you are looking for data scientists. Thus, they have the skills, but they use different words to describe the types of techniques they use; for instance, they might use a different vocabulary to describe the statistical techniques that they understand. This is the main reason why it is hard to recognise from the CVs of people from domains other than computer science that they are a good fit.

Decoding Untapped Data Science Geniuses

At Smart Tribe, we have identified many of these people who are a fantastic fit in data science roles, and we’re in the process of making their skills visible to companies. 

Due to this process, suddenly, there are many more super qualified data scientists who are able to plug in and do fantastic work right away. We even argue that they are often more necessary additions to the data science team as they add diversity in the ways of thinking and solving problems. In this context, there is a growing consensus that there should be more diversity in teams, especially when building products or services that will be consumed by a diverse range of the public. 

Thus, it is clear that academics are the secret on how to address the highly unsatisfied demand for data science talent.

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