This guide brings together proven strategies from Talent Acquisition leaders at top companies for increasing diversity on your engineering team.
Data-Driven Recruiting for Data-Driven Companies
Connecting Physical and Digital Technologies
To say that Stanford’s StartX is the best startup accelerator out there isn’t a statement of opinion–it’s a matter of fact. 92% of StartX’s venture-backed startups continue to grow (or are acquired) at the 10-year mark; StartX startups have a 2.6x higher chance of reaching a $100 million valuation than alumni of other leading accelerators; and they’re 60% more successful at reaching Series A than the industry average. Thus, 2016 alumni Arundo Analytics’s success should come as no surprise.
Founded by Stuart Morstead and Tor Jakob Ramsøy–who both spent more than a decade refining their skills at management consulting firm McKinsey & Company–Arundo provides IoT solutions for asset-heavy industries such as oil, gas, and power. By bringing advanced data analytics to industrial operations, Arundo helps companies make data-driven decisions that can help them boost productivity, reduce waste, improve environmental outcomes, better maintain their equipment, and more. With offices in both the USA and Norway and customers that include Ineos, ABB, and Accenture, Arundo’s growing fast–meaning they need to hire fast as well.
Co-Founder Tor Jakob Ramsøy
The Data Science Hiring Dilemma
With a business built around data analytics, it’s no surprise that Arundo’s rapid growth brought with it the need to recruit technical talent–particularly data scientists. Data science and engineering comprise “just over 50% of our company,” says Cody Falcon, Arundo’s VP of product. Cody’s been with Arundo since its graduation from StartX in 2016 and, as such, deeply understood the sort of candidates that would best fit Arundo’s needs. To keep pace with growth, Arundo needed to double its workforce–from 50 people to 100–within eighteen months.
Hiring at scale for data science roles presents unique challenges. Evaluating talent is difficult because what exactly constitutes top talent within the field can vary considerably–are math skills most important? Should engineering know-how take priority? How much subject-area knowledge should a candidate have about the company they’re applying for? Because data science is still an emerging field, these considerations don’t yet have clear-cut answers, making recruiting data science talent an undertaking that can quickly become complex, time-consuming, and unreliable in terms of results.
Though a young field, data science is booming. Data science positions often get flooded with candidates–a single role can generate hundreds, even thousands of applicants, making top-of-funnel screening a daunting task. “We have had an enormous number of applicants for the positions that we’ve had open, so the biggest issue just has been trying to assess all of those candidates in a way that was efficient and fair,” says Roy Keyes, head of data science at Arundo. With a deluge of candidates and a hard-to-fill specialty, Roy and Cody knew they needed to find a better solution to help their small team meet their big goals.
We have had an enormous number of applicants for the positions that we’ve had open, so the biggest issue just has been trying to assess all of those candidates in a way that was efficient and fair.
— Roy Keyes, Head of Data Science
Efficient, Equitable Solutions--Powered by Data
Arundo partnered with CodeSignal in 2018. They utilize CodeSignal Pre-Screen as a top-of-funnel candidate screening tool for all technical candidates, from developers to data scientists. With a library of over 4,000 questions and support for over 40 programming languages, Pre-Screen is a powerful and flexible tool that helps Arundo leverage role-specific screening assessments quickly and easily. Cody notes that Pre-Screen “allows [Arundo] to reach more candidates and comb through and identify the top performers a lot earlier in the process, which ultimately saves time in our recruiting efforts”
CodeSignal’s user-friendly interface and ATS integrations also help Arundo streamline their hiring, allowing them to move quickly and compete for top talent. Roy states that CodeSignal’s integration with Greenhouse “just makes things a lot more smooth and efficient” when it comes to sorting through a wide talent pool and that Arundo’s hiring manager can “easily send [candidates] a CodeSignal assessment with just basically one click.”
More than speeding up their recruiting process, though, Cody points to a different aspect of Pre-Screen as the key benefit of using CodeSignal: the boost to equity and inclusion they’ve seen in their hiring process. Cody states that, by using Pre-Screen rather than resumes as a top-of-funnel screening tool, “CodeSignal has allowed us to really level the playing field for all of our inbound candidates.” Additionally, Cody remarks that Pre-Screen “gives us additional insight beyond your traditional coding test.” Thanks to Pre-Screen’s recording and replay features, they’re “able to look back […] to better understand our candidates’ problem-solving process and how they’re thinking through the problems.”
Diamonds in the Rough
As a company that’s already all about data, CodeSignal’s data-driven hiring solutions have proven to be a natural fit for Arundo. Using Pre-Screen as a top-of-funnel screening tool and Interview–CodeSignal’s powerful, all-in-one remote interviewing solution–for technical interviews, Arundo has been able to grow its workforce “by more than 100% in the last 18 months” according to Cody, who states that CodeSignal has “allowed us to identify the diamonds in the rough and quickly crank through a very large candidate pool.”
Recently listed alongside tech giants like IBM, Microsoft, and Google as a top player in the AI in IoT market by the Daily Chronicle, Arundo is poised to continue growing and innovating. Arundo’s streamlined, equitable hiring process powered by CodeSignal ensures that they’re ready to scale their workforce for whatever their future holds.