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Great Resignation: How to Be Successful in Attracting Top AI and ML Talent

Published
Apr 27, 2022
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It has always been challenging to find the top artificial intelligence (AI) and machine learning (ML) talent -- and today’s environment has only heightened the difficulty. Non-tech companies have increased their demand for these workers, even as tech powerhouses like Google, Facebook and Amazon seek to hire thousands. Companies must broaden the funnel of potential candidates by making themselves more appealing to potential top talent workers. Despite the high demand for AI and ML workers, essential skills, expertise, and experience are scarce. According to a recent Gartner's 2021-2023 Emerging Technology Roadmap Survey that cited talent availability as the main adoption risk factor, it's no surprise that AI and ML professionals are in high demand at companies utilizing (or seeking to utilize) these emerging technologies, regardless of whether they're just getting started or have a lot of expertise. It’s essential for firms, both tech and non-tech, to be creative in their approach.

Here are three tips for recruiting AI and ML talent:

Leverage Open Technology Community Groups

Find where talented AI and ML engineers and data scientists hang out. For many companies, at first, this can be difficult to identify, but through resources like Meetup.com, firms can find groups where engineers and data scientists congregate. Meetup.com is a platform where groups of users focused on a certain topic get together and organize events – and is a site which has been used to build professional tech community groups. Firms can find dozens of AI and ML networking communities in all large cities. It’s important for firms’ hiring managers to be involved in networking within these groups and socialize, letting other users know why your firm is the best place to work!

Create Partnerships with Top Technology Universities

Invest in creating partnerships with top tech universities for recruitment. An example of this is collaborating with data science graduate programs at local universities. From there, you have the first pick of the top talent straight out of the universities. This can provide an unlimited technology talent pipeline and connect you with the best student picks.

Commit to a Culture of Diversity and Digital Innovation

When interviewing the talent, it’s important to paint a clear picture of a culture of digital innovation and share why their work is worth it. This shows the candidates that workplaces are passionate about helping transform their clients' businesses using emerging technologies. For example, give references of AI and ML use cases that team members have contributed to which provides value to the firm and clients.

As you invest in AI talent promotion and development, collaborate with your human resources team members to personalize an approach to implement AI skills at work to meet the changing expectations that the industry faces. This will lead to the promotion of internal team members and provide them the advancement of AI skills and training needed to take on roles like data scientist, data engineer, ML engineer, and business intelligence analysts.

Finally, an open innovation culture attracts top tech talent, regardless of the individual’s race, gender, or background. It shows firms are passionate about the solutions they build that drive a fantastic client experience. When it comes to recruiting AI and ML talent, firms should no longer try to compete with the big tech companies like Amazon, Microsoft, Facebook, Google, and IBM. However, a more viable approach is to collaborate with leading technology companies, allowing teams to work on best-in-class AI and automation solutions from these big tech companies. With the digital revolution that COVID-19 has kickstarted, there is an opportunity for all companies to establish a strong reputation for digital excellence through the recruitment of an open, innovative, and diverse new workforce. As your reputation gets better, your opportunity to attract top AI and ML talent will be more significant.


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