Data Scientists are Companies’ Heroes
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There is no doubt that the future will be shaped around Artificial Intelligence, with our way of life slowly changing. While most people are starting to fear for their jobs, it seems that one profession rises above all, and is almost seen as indispensable in most companies. You guessed it. If you are a data scientist today, the chances of you not finding work is minute.
One cannot help but wonder if the importance given to data scientists today is exaggerated, or are they really deserving of the praise?
Let’s start by understanding what a data scientist is and what her/his job entitles.
Data scientists analyze data and help create prediction models for companies to forecast their future. They help the latter make more focused and efficient business decisions, while putting together strategies based on facts. By means of ‘trial and error’ (referred to as hypothesis testing), they can also figure out what works best for specific groups of people and focus the efforts on strategies that are highly probable to succeed.
Data Scientists have a lot to work with nowadays, using both internal and external data.
While companies hold a lot of information regarding their past activities and sales (‘internal’), data scientists can help orient them towards successful paths, enabling them to focus their spendings in the right area. In other words, they can analyze the people that are most susceptible to buy a product, invest in a service, etc.. Efforts are redirected towards the right audience or appropriate market: by analyzing and understanding the past, they are able to somewhat predict the future.
Furthermore, an abundant amount of ‘external’ data can be found as well. How many of us have willingly shared information just to download an application, gain a free coupon or get a credit card? Even more so, some firms are solely specialized in data collection: whether it’s demographics, behaviors, economical status, likes, dislikes, hobbies, past education, friends, etc., anyone can now buy that information.
According to Dr. Lobna Karoui, “Data scientists are crucial to any company but we need to clarify that when building new business models, the disruption or innovation of processes is not solely dedicated to them. After collaborating with more than 20 functional and business areas (HR, sales, M&A, communication, trading, etc.) on building AI products, I observed that the main reason for each of the success stories was the collective deep belief on the necessity of the AI project with a main focus on reasonable iterations and the final outcome. Such Growth Mindset is essential for any new AI venture”.
In a 2017 emerging jobs report, Linkedin pointed out that data science jobs increased by 650% since 2012. It seems that in 2018, only in the USA, there was a shortage of more than 150,000 data scientists (also according to LinkedIn). In addition, IBM estimated that the need for data scientists from 2019 to 2020 will grow by around 30%. The main reason they are so in demand is that they are not specialized in one aspect and their set of skills can be interchangeable. Whether governmental, financial, insurance, education or marketing, it seems that their added value makes them one of the most demanded professions today, no matter the industry.
Back in October 2012, Harvard Business Review named data scientist “the sexiest job of the 21st century.” Moreover, with the likes of Google, Apple and Amazon regularly in the press with stories of rapid advancements in artificial intelligence, it is easy to assume that data science jobs must now appear on the personal career radars of almost everyone.
Does that mean being a data scientist makes you immune to the changes that will arise from the future? Just like many jobs, data scientists will gradually end up being replaced. Some companies are finding ways to clean and automate data without the help of that many people (i.e. DataRobots or H2O). If a profession that still seems to be booming is also at risk, a good question we can ask ourselves is: how can we be prepared for what’s next? We shall look into it in more details in our next article in the series ‘AI for Everyone’.
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