Sourceress raises $3.5M to find candidates that managers want without realizing it

When a company is looking for a candidate for an open role, the employ manager is likely going to rattle off a bunch of qualifications that they’re looking for to a recruiter — and Kanjun Qiu says recruiters will probably just run with that when the manager’s requirements might not actually be so rigid.

It’s that intent from the manager — the idea that the actual borders for a qualified nominee are more opaque — that triggered the idea for Sourceress. Instead of only hunting down candidates based on a bunch of keywords, Sourceress works with hiring managers to understand the kinds of attributes they need in a potential hire and constructs a model to discovery someone who would fit what a employ director is looking for, even if they don’t fit the bill explicitly. To do this, Sourceress has raised $3.5 million in new financing from Lightspeed Venture Partners, OpenAI researchers, Y Combinator, Dropbox founders Drew Houston and Arash Ferdowsi, as well as other smaller investors.

“The advantage is that when you source and you go outbound as a company, people feel like, oh, you want them, ” Qiu said. “You’re extending a hand out to them, and then they can choose to take your hand or not take your hand. It builds you feel like you’re wanted, that you have these options, that you could go somewhere. The problem today is sourcing is so transactional, you hire sourcers who are on contract or not on contract. It’s hard for you as a sourcer to spend time personalizing and customizing an approach, and the tools aren’t truly there.”

For example, simply because person doesn’t have experience with a specific programming language doesn’t mean they can’t be trained in that language. So, rather than totally ignore a candidate since they are don’t have experience working in JavaScript, Sourceress should pick up on a candidate with years of experience using Python and flag them as someone worth flagging as a potential hire. The same might be true of a qualified nominee with experience utilizing that language, but fewer years than what a company defines for its initial standards.

The problem starts with a phone call with a hiring manager, where that person will detail to Sourceress what they want in a candidate. Sourceress then constructs a model based on that information and starts scouring for nominees on the avenues that you might expect, trying to bend the boundaries so they aren’t so rigid in their search for candidates. Each additional hire tunes those algorithms over time to better look for nominees. Right now, Sourceress focuses on engineering and product — because, for now, it stimulates sense to be working in an area where the team has experience.

It’s that tuning proportion which is probably the most critical facet of Sourceress’ future. Having to take a bellow with a hire administrator every time can be a ache, especially as more and more hire administrators call in and are genuinely looking for nominees with very similar profiles. As Sourceress matches the right nominees, its notion of what a director that wants when they ask for “a Python expert” will start to better understand the intent behind their search for a candidate, rather than just taking the qualifications at face value. The models were becoming increasingly abstract, and eventually, once Sourceress has enough data, it can automatically divine the right candidate profile.

Right now, there’s no candidate-side part of the service, as the low-hanging fruit is more on the recruiting side. But it would make sense to employ such a model to slot into the places that Indeed, Hired, or even LinkedIn, have tried by dedicating nominees a hub to go and find potential undertaking matches. Most potential hires are passive nominees that aren’t looking, and it’s hard to determine who to reach out if they aren’t raising their hand, Qiu said.

Taking this kind of approach by go looking for potential attributes — and not just qualifications — is something Qiu said would help surface up more diverse candidates, which she said tend to have a higher response rate. Qiu also said the percentage of our hires for women and minorities on Sourceress is between 30% and 40%.

“Women, when they look at a job description, they are generally disqualify[ themselves ], ” Qiu said. “So if you’re reaching out they’re more comfortable talking to you. If we’re able to actually assess for merit, and we’re be permitted to fill the top of the funnels with more women or minority nominees, your likelihood of hiring person goes up. If you’re not get diverse candidates into the pipeline, it’s hard to attain diversity hires. The problem is most pipelines, they’re referral based. Coming into this, we guessed, if we can construct procuring candidates get in touch with them much easier, we should be able to change.”

Since it’s a language problem as much as it is an unstructured public-facing data problem, it’s going to be an area with intense competition. There are startups like Headstart looking to help analyze nominees, though that process more profoundly involves presidential candidates side in order to ascertain the right fit. There are, indeed, a lot of startups getting money in this space — and it’s likely that plenty of the bigger companies are working on such tools.

The end goal would be, for example, for Sourceress to be able to find a student at a college in the midwest that they are able to either immediately or one day fit the needs of a hire manager. That might necessitate scouring a Github account, or published newspapers, or what kinds of posts they put up on Stack Overflow. But the point is to come up with a diverse situate of information sources that can help the company identify candidates that a recruiter might not find if they were just digging through LinkedIn for potential results. All this data would naturally be public-facing, which entails it could be up for grabs for anyone, but in the end, it’s the approach that are important more, Qiu said.

“The actual data itself doesn’t matter, it’s how you post-process it and the features you extract, ” She said. “That’s our meta processing layer, that’s the difference.”

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