This Founder Has Great Advice For Assessing Top Technical Talent For Your Startup

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Finding highly-qualified technical candidates for data science and machine learning roles is difficult for technology companies of any size. Edouard, 30 and Jeremie Harris, 28, aimed to create a marketplace called SharpestMinds to match companies to promising technical candidates who are mentored by experts in the field. SharpestMinds allows individuals who have a strong background in programming to be paired with experienced data scientists at leading companies to be mentored, with the mentor getting a certain percentage of the mentee’s income if they find gainful employment at a tech company. The startup was a part of Y Combinator’s Winter 2018 batch and has gone on to raise $200,000 from YC and various angel investors.

Frederick Daso: How does your startup go about finding and then assessing technical talent for hiring purposes?

Edouard Harris: In searching for technical talent, we tend to find these individuals in specific social networks, which are quite extensive in this field, and eventually hire them ourselves. The traditional interview process tends to be usually based around super inefficient exercises, and as a result, most companies interviews don't correlate well to on the job performance.

What we do instead is we bring folks on a contract basis or a few months at a time. If the person that is contracted out a does something throughout the contract period that surprises us in a positive sense, we hire them. If not, they don't, and it's no hard feelings because the expectation is that they won't get hired for a full-time position.

That's the process that we use to hire because we're assessing for how well the person actually is delivering work for a company, which is the thing that's going to correlate the most to how they're going to provide results for the company once they're employed.

Daso: It sounds like your process is straightforward versus like the traditional, five, six round technical interviews and onsite interviews to determine whether an applicant is a good fit. You mentioned earlier that, like the conventional hiring process for these professional roles as based on superstition. Could you elaborate more on what that is and why it's inefficient and finding the right talent?

Harris: It's just not going to be as efficient as it could be. Giant tech companies do this because they have essentially infinite amount of inbound. There aren't actually that many companies who can afford to apply these stringent cutoffs because those cutoffs eliminate a large number of extremely competent people.

Daso: Explain to me how you have created SharpestMinds to solve traditional hiring process issues?

Harris: We are a marketplace where a senior machine learning engineers can train up early stage machine learning engineers, usually fresh grads. They're folks who are just out of school, and they get mentored by an experienced engineer for free upfront in exchange for a small percentage of the mentee's first-year of salary if they get hired. We're the world's first marketplace for income share agreements, starting with machine learning.

Daso: Income share agreements in machine learning mentorship, I see. In that regard, how did you guys see a mismatch in the demand and supply for machine learning and AI software developers? How did you come to the conclusion that a marketplace where senior engineers can train early stage engineers would be viable?

Harris: We started out with what was mostly a placement model for machine learning engineers, and it was better than competing models because we had good technical filtering of talent. At the end of the day, it's not enough to better. You have to be different along a clear axis.

The catalyst for our pivot came when we started to notice companies going directly to candidates to avoid placement fees. So, we took a step back and said, okay, well, how did the best operators in the industry solve this problem for themselves? There are plenty of companies out there, how do they solve this problem? And it turns out interestingly, they actually don't solve this problem. This is just a permanent problem in the industry.

Daso: My last question is for the founders in the earliest stages of their company, whether they're still in school or they've recently graduated. If you could give them any advice and hiring technical talent, what would it be?

Harris: For early-stage first time founders, my primary advice for hiring technical talent is to network as much as possible. That's probably the best pre-filter for finding viable candidates. It's just going out to meetups, networking, talking to people when people aren't trying to interview for positions explicitly.

They tend to be much more real than during an actual interview. That's one kind of interaction that I found generate useful data. I find it's much better to have a conversation with the person and then when the need arises, they are the person that pops into your head from the conversations that you've had.

This interview has been lightly edited for clarity and readability.

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