Diamonds in the Rough: The Key to Recruitment Success
15 March, 2022
Struggles Startups Face Attracting Quality High-Demand Talent
Data science, software engineering, and many other tech roles are highly in-demand professions, despite the pandemic-era slowdowns.
The problem that we had (and continue to have) is a combination of two things:
- Recruiting engineers and techies are competitive. We have to compete with a lot of companies 一 including the tech giants 一 who are flush with capital. Startups can find it difficult to offer salaries that keep pace with the market, and big tech stock growth can even offer total comp promises in the neighborhood of successful startups.
- We are in need of people who have a variety of skills. As much as we are on the lookout for traditional web engineers, simultaneously, we often individuals who are skilled in multiple domains, such as one person who understands machine learning, cloud computing, and computational biology all at once. Our ideal candidates are often those who have a hybrid skill set, or people who have an advanced background in more than one topic.
Finding ideal candidates is like finding a needle in a haystack, and when we did find some, there was often a high degree of competition for them. We found it tough to get our offers accepted by these candidates.
Recruiting For High Potential
To source great candidates for our open positions, we’ve taken a few steps to find people who we thought could become great fits, even if they weren’t completely perfect yet. We wanted to grow promising candidates into the ‘perfect’ employee that we were looking for by recruiting for potential, in addition to recruiting for demonstrated excellence.
First things first, we identified the key traits that we were searching for in our candidates that are hard to teach. Anyone can learn a new programming language, or web framework, but we can’t teach qualities like willingness to learn, strong work ethic, or fundamental intelligence. People can only really develop and grow those qualities on their own.
For instance, if someone has powerful faculties for working with data and machine learning, but their last biology experience was high school biology, they might only need to learn a little bit of practical information about working with genomic data to be able to bring their skills to bear for us. Figuring out the intangibles and the hard-to-learn skills in any open position and listing those out is step one.
Once we know some of the intangibles that might be important for a role, the next step is to design the interview process to dig into those qualities. We designed our interview process to test three main things. First, we assess the technical basics needed for a role, such as general coding, basic algorithms, or web architecture, to ensure they had the fundamentals needed to be able to engage effectively in the space. Second, we ask more open-ended questions to see how a candidate thinks and solves problems within their area of mastery, to see how they behave within a domain they know well. Do they show brilliance? Do they always get to a great result through dogged determination? Do they find ways to learn about new concepts and connect disparate ideas? Third, we asked behavioral questions, like how they solved a specific problem in the past or dealt with previous failures they experienced. Apart from the more technical questions, behavioral questions pin down how the candidates solved problems in real life. Taken together, these three kinds of questions give us a clear indication of how much potential someone has to grow into an outstanding contributor, even if they don’t already have the boxes ticked on their resume.
When going through this process, it’s a great idea to think back on your career experiences and people you’ve known who you think would be great for this role. Ask yourself about the qualities that the person has that make you think that. It could be that the person is great at C++, or the way they approached a certain kind of a problem, or a personality trait that made them well suited to a certain kind of work. . Once you have a sense for those qualities, then figure out which ones are intangibles, and how you’re going to interview to find them in someone else.
Another important point is that during the recruitment process, when we found candidates who we thought had the potential to become fit but weren’t yet, we’d clarify that we recognized this potential in them and wanted to help them to grow. Perhaps if a candidate with a strong computational biology background applied for a data scientist position, but had never done machine learning, we’d tell them that we were considering hiring them into a different role for six months while they completed some company-supported machine learning training to prepare them for a data science role. Critical to this promise, of course, is to follow through with the training and, assuming they complete it successfully, the promised role. In the end, we want to hire outstanding people who love the company, are excited about their work, and are willing to go the extra mile, and this process has enabled us to do that on multiple occasions.
Help Others Grow, and They Will Help You Grow
- When you do invest in someone, help them build up and achieve their career goals, they’ll start to feel a sense of loyalty. It helps to start off the employment relationship on the right foot, as long as anyone follows through. Not only do you find someone who can shortly grow into your ideal candidate, but you help them develop in their career. It’s a win-win.
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