LinkedIn recently published a report naming the fastest growing jobs in the US based on the site’s data. The social networking site compared data from 2012 and from 2017 to complete the report. The top two spots were machine learning jobs, which grew by 9.8X in the past five years, and data scientist, which grew 6.5X since 2012. In all the top ten positions, four relate to data science and three out of those four are in the top five spots. So why are data science positions, and specifically machine learning positions, growing so fast?
While reports and publications consistently name data science as one of the top jobs in the country, LinkedIn’s report is unique in that is shows the dramatic growth in this position. Here are four reasons why machine learning and data science are the fastest growing jobs.
The amount of data has skyrocketed
Not only has roughly 90 percent of the data created in the last two years, but current data output is 2.5 quintillion bytes of data daily. To wrap your head around that number, here are some numbers from data company Domo:
- Every minute, Americans use 2,657,700GB of data.
- Every minute, Instagram users post 46,750 photos.
- Every minute, 15,220,700 texts are sent.
- Every minute, Google conducts 3,607,080 searches.
Each of those actions produces data, which makes the amount of data simply mind-boggling. Because of this tremendous amount of data, companies need people that can do something with it. For example, Instagram wants to know of those 46,750 photos posted in one minute, which get the most shares? What type of content works best on the platform? And that is only the tip of the iceberg in terms of what information can be gathered from data. Because the amount of data has grown exponentially, so has the need for people that can read and analyze it.
Data-driven decisions are more profitable
In the end, for many companies, data is not useful unless it is beneficial, which it certainly is. Data not only helps companies make better decisions, but those decisions also usually come with a financial gain. A study by Harvard Business Review found that “companies in the top third of their industry in the use of data-driven decision making were, on average, 5 percent more productive and 6 percent more profitable than their competitors.
Instead of relying on the CEO’s gut instinct, data allows for nonpersonal decision-making based on numbers alone. If data can make companies more profitable and competitive in the market, that is undoubtedly a reason why more companies hire employees that can handle reading the data and putting it in layman’s terms, so the team can understand how to move forward.
Machine learning is changing how you do business
Machine learning is a type of artificial intelligence (AI) where the systems can actually learn and evolve. Machine learning has infiltrated many industries, from marketing to finance to health care. The advanced algorithms save time and resources, making quick, correct decisions based on past learnings. Financial institutions, for example, are eliminating the need for traditional loan officers, as machine learning algorithms can assess risk and make the decision without the need for a person.
Machine-learning-as-a-service (MLaaS) is a now a reality, and more companies are using such platforms instead of investing the high amount of resource and skill to create their own machine learning platforms. The average business person can use machine learning platforms to make smart decisions without the need for C-suite executives’ input. The way we do business will completely change, though we will still rely on those developers who can create machine learning algorithms to advance the technology.
Machine learning provides better forecasting
Machine learning algorithms often find hidden insights that went unseen by the human eye. With the vast amount of data being processed, even an entire team of data scientists might miss a particular trend or pattern. The ability to predict what will happen in the market is what keeps businesses competitive, and machine learning algorithms can make that a possibility. Businesses want machine learning experts who can continually improve forecasting models to gain an edge over the competition and continually stay ahead in the market.
Data science and machine learning jobs will continue to grow for the foreseeable future. Given the vast amount of data and its profitable uses, companies will always be on the lookout for candidates to fill these roles. The demand, however, clearly outpaces the supply, as McKinsey Global Institute estimates the US could have as many as 250,000 open data science jobs by 2024. The data science skills gap leaves companies scrambling to train or hire candidates who can meet their analytical needs. As bootcamps and online programs try to fill this gap, businesses may find themselves vying for talent in the coming years.
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