Hackajob, the Techstars London alumni, claims to have built a platform to make hiring technical talent more “intelligent and data-driven”.
It does this in part by holding weekly online challenges related to specific technical expertise and based on the data these challenges produce and the startup’s own tech designed to assess code quality and other criteria, matches technical talent to job openings at companies who are hiring.
To help further its mission to make recruitment more transparent, Hackajob has picked up £400,000 in seed funding led by Downing Ventures. Doug Scott’s Potential VC also participated (even though Scott doesn’t believe in venture capital, apparently) along with the tax payer-funded London Co-Investment Fund.
The round follows earlier angel backing, and investment from Techstars as part of its accelerator program.
“With competition for technical talent so fierce, our approach helps companies identify talent, who they may have previously missed after judging them on paper, as well as diamonds in the rough,” Hackajob’s founders, Razvan Creanga and Mark Chaffey, tell me in a joint email.
“We host weekly ‘hackasprints’ – online challenges that allow candidates to show their ability and get the job they deserve based on actual skill sets. This makes the recruitment process more transparent, with companies being able to view their leaderboard of candidates ranked objectively. Companies also have the ability to search through our community of more than 25,000 users. With several leaders in the tech industry calling out for more diversity, we hope our platform can provide hiring managers with more data that will provide a level playing field to all candidates, regardless of their age, ethnicity, gender or background.”
In other words, regardless of the question — in this case, how to identify and hire technical talent — better data is the tech industry’s answer. To make this a reality, the Hackajob founders say they have built tech that can objectively assess code, looking for things such as “code quality, efficiency and industry best practices”.
And they don’t plan to stop there. “We are currently exploring how Artificial Intelligence could be used to identify traits, trends and patterns in successful candidates, and then scale this back to identify future talent, who are much earlier in their career or still studying, although this is at very early stages,” add Creanga and Chaffey.