David Kuntz, Head of data science at Degreed, discusses how to find and form a close-knit, full-stack data science team in weeks.
We’ve all heard about data science unicorns – people with an almost mythical set of skills that can bring real clout and power into your organisation. But searching for your unicorn can be a misguided mission. Instead of hiring just one unicorn, who are hard-to-find and often out of budget, look at creating a full-stack team and uniting their differing skills and experiences towards a common goal.
At a key time in Degreed’s product journey, we had a critical need to build-out our data science capabilities to accelerate its product roadmap. So we took this approach, searching for ‘full-stack’ data scientists who were diverse, collaborative and talented. Then we united them as a close-knit team under significant pressure and company-wide scrutiny. Simultaneously, the Coronavirus pandemic began to spread. As our data science team began their life at Degreed, so too did the global shutdown.
Looking back at this time, there are lessons learned for all leaders looking to build a data science team quickly. In recruiting, uniting, and motivating them – even when spread across different geographies and time-zones.
We were fortunate in that there was a clear business case to invest in a “full-stack” data science team. There was senior buy-in from the start, which is critically important.
We were looking for people who were not just strong data scientists, not just experienced ML (machine learning) engineers, but also talented software engineers – people who had strong coding and product development skills. And not just that, but people with strong critical reasoning and critical thinking skills.
The team’s main focus is to build production-grade ML-driven services to create new capabilities and features in the Degreed product, initially for the (currently in-beta) Career Mobility product that helps to link skills and learning to work opportunities.
When building a team from the ground up there is a temptation to focus on hiring a collection of very experienced people, especially when the team has a lot to do, and needs to move quickly. But this can be a mistake. Diversity in the team, along many dimensions, allows each person to contribute from the start.
The benefits of a diverse team are well-known. Diversity leads to greater innovation. Culturally diverse teams, for example, are more likely to develop innovative new products compared to homogeneous ones.
“…Among analytic and business intelligence (BI) leaders indicates a positive relationship between the diversity of teams and business benefits.”
She goes on to explain that data leaders must consider both the ‘seen’ diversity criteria (gender, ethnicity and so forth) as well as ‘unseen’ factors like the diversity of thought, knowledge, experience, cognitive styles and perspectives.
It is especially important when building this kind of team to blend areas of expertise and experience so that each person on the team has something to teach the others, and something they can learn from others. This provides balance and trust across the team and helps create a learning culture, which is especially important when developing groundbreaking new capabilities.
What does it take, then, to find people with different skill sets, experiences, strengths and personalities?
To build a varied team that would be better at problem-solving, coming at an issue from different angles, where there are fewer blind spots, they can learn from one another, and their skills complement each other?
Doing this today requires really reading CVs in detail. It might seem old-fashioned, but each CV tells a story that mere keyword filtering cannot yet capture, nor yet most of the AI (artificial intelligence) applications in this area.
