One of the toughest challenges for employers to navigate is how to find freelance talent that’s high-quality. When contract workers or freelance help is needed, it can be even trickier knowing where to identify top-tier candidates and determine which person is the best match for a particular position.
Difficulty Finding Freelancers
Medium notes, for example, that a major problem for employers who need to hire freelancers is that they feel that “choosing a freelancer is painful.” In the old world of pre-tech solutions, this may have been true. Hiring managers and recruiters used to have to rely primarily on manual methods like word of mouth, or posting job ads in career publications or websites where contractors would be likely to see them. Even when using websites that are designed to present a smorgasbord of freelance talent, employers are left to try to sort the wheat from the chaff in a time-consuming process of reviewing materials and comparing credentials.
But with the emergence of new technologies and apps in the talent search marketplace, it may now be much easier for companies to find the perfect-fit freelancer for their projects. On the flip side, such tech tools can also pave the way for freelancers to zero in on the right work for their skill sets.
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Using New Tech Tools to Find Freelance Talent
One new tool that employers should take the time to explore is the recently launched “matching algorithm” from Field Nation. Field Nation, which specializes in connecting businesses with service providers and partnering with them to complete projects, states that this is the “first and only” artificial intelligence (AI) algorithm of its kind that leverages big data and machine learning in order to pair work orders with talent. The result is that employers are ostensibly able to locate the best field service contractor for the job, mitigating their risk while opening the door to quick access to quality freelancers.
The matching algorithm works by tapping into a decade worth of client and service provider data. Based on these vast data sets, the algorithm has the ability to see a contractor’s past performance, client ratings, and proximity, and compare those with the employer’s job requirements to come up with a recommendation for the best service provider for the position.
The algorithms are able to identify a predictive quality score for each provider by applying machine learning techniques to this data. In this targeted approach, the score represents the service provider’s value for each work order submitted, with higher scores indicating higher quality contractors. The matching algorithm next uses this information to suggest a list of service providers with scores that reach or exceed the employer’s requirements.
The new Field Nation matching algorithm stands out from other tech tools designed to help match freelancers with employers, such as Upwork (formed from the merger of oDesk and Elance), which provides more of a global cataloguing of freelance talent without the predictive AI ability. With this in mind, the emergence of more advanced platforms specifically designed to provide a deeper level of service for employers conducting freelance talent searches suggests a trend of which every competitive employer should take heed: the freelance workforce is clearly continuing to escalate in importance in the labor market.
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