Data science and machine learning are topics making rounds within tech industry circles. In early 2019, Indeed published their annual Best Jobs insights based on the previous year’s industry performance. To not many people’s surprise, the machine learning sector ranked very highly – in fact, it is listed as number 1 out of a selection of 25 jobs.
According to the research, Indeed cites a 344% growth in the number of job postings for the role of machine learning engineer, with most candidates having a master’s degree or online course certificate as part of their qualifications.
With demand for data scientists and the like at an all-time high, how can hiring managers look to the future to secure the best talent in this increasingly competitive market? Here are our predictions on the future of machine learning hiring.
Changing Academia Syllabus
The development of machine learning skills and demands has happened at such a fast pace that academic courses are under pressure to adapt their programmes in order to teach students up-to-date skills. These students will be increasingly exposed to software engineering, statistics and linear algebra for example. Within the next decade, we expect there to be a higher number of graduates capable of applying machine learning without even having complete expertise in the full field. With more people being taught foundation tech skills, such as in AI, companies will have a facilitated and cheaper access to crucial talent.
On trends in recruitment, Associate Director of Fyte UK Tim Clark says that “at Fyte, we are witnessing an increased demand in specialist and niche knowledge in the context of the broader sector field. Natural Language Processing, for example, is a skill that has seen a greater competitive edge over general machine learning qualifications.”
Upskilling non-AI Professionals
In their market research, PwC concluded that upskilling existing data professionals, who are not strictly AI-specialists, to have a broader understanding of AI has become a crucial part of workforce strategy. 47% of targeted workforces are said to change their performance and development frameworks to AI skills, such as basic machine learning.
Anyone can essentially begin machine learning by getting out old xls/csv-File/Database exports and plotting the data. It may look trivial, but this could be your first step in a machine learning process.
Research still suggests, however, that for an effective and data-savvy workforce, companies need to plot out different levels of AI-competent employees. So while existing employees of some companies can expect to be taught foundation skills in their learning & development, companies are still eyeing new talent that can accommodate more challenging practices.
The monetary potential of machine learning roles are already significant. In their US-targeted research, Indeed estimates the average machine learning engineer base salary to be $146,000. The machine learning market is predicted to grow to $8.8B by 2022 from $1.4B in 2017, according to a report by Research and Markets.
On the salary of entry-level candidates, Tim Clark comments that “PhD and Masters students remain largely favoured when it comes to salary competition. Whilst their salaries are indeed high and healthy, there are indications that the growing number of students pursuing this sector has slightly plateaued salary figures.”
This article is the first of a series focused on the competitive machine learning market – watch this space for more of our insights!
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