It was predicted by the World Economic Forum that at least 133 million new roles generated as a result of the new division of labour between humans, machines, and algorithms may emerge globally by 2022. If true, it shall compound the already massive shortage of people skilled in data science and AI. As reported by Deloitte in 2020, for the second time in four years, the number of jobs posted by tech companies for analysis skills, including machine learning (ML), data science, and data engineering, surpassed traditional skills such as engineering.
While these stats are concerning, today’s hybrid working environment provides us with an opportunity to broaden the talent pool within organisations. Previously we were fighting for talent in a localised area, and all of a sudden, these boundaries and borders have opened up. Hybrid or remote working enables companies like ours to access talent from anywhere in the world. Nowhere is out of bounds. More cultural diversity on our teams also helps elevate our solutions and forces us to think globally in order to drive a better data-driven customer experience. We are definitely seeing many benefits from remote-first working.
But these new ways of working can also create challenges – especially in the recruitment process. We see many CVs with a laundry list of impressive technologies, but this is only the tip of the iceberg in terms of what we look for in a great candidate. Learning about a candidate’s “hidden” dimensions of systems design, problem-solving, and engineering value system takes time and effort. It can’t be extracted from an A4 resume. The recruitment process moves fast, but going the extra mile to learn about candidates and visa versa is essential if retention is important. Considering the onboarding of new recruits is cost a company on average £3,500 per hire and the impact of a high staff turnover on company morale, it should be.
If the World Economic Forum’s forecast is to come true this year, addressing the serious tech skills gap is essential. One way to overcome the shortfall is by employers adopting explicit strategies to identify and nurture skills among staff without traditional education qualifications. Providing opportunities for existing employees to learn a new skill is both rewarding and necessary to keep up with the competitive and fast-evolving tech landscape. An infusion of established skills can help expedite and increase the effectiveness of on-the-job training but this will not generally scale. Every situation is a bit unique, but by building a culture of curiosity and incentivizing employees to share their learnings with others, you can create a viral multiplying effect of upskilling across larger teams and organizations.
In today’s highly competitive and evolving market the state of affairs is not clear cut. Companies can no longer simply post a job description and wait to fill it. Organisations must start by finding a person with the right skills and mindset and attitude and help them accomplish their professional skills in a role.
About the author on AI talent in a hybrid working environment: Ryen Gunning, Director of Data Engineering at Vista
Ryen has been part of the Vista data and analytics transformation since joining in 2012. He found his passion for data and technology during his prior 6-year consulting career with Accenture wearing many “data hats” including business analyst, product owner, and data developer. Today, as Director of Data Engineering at Vista, he leads a global engineering team responsible for building world-class data products on a data mesh architecture. He holds a BS in Business Administration – Operations Technology Management from Boston University.