Unicorn Farming: Building Capabilities Against the Odds (Part 2 of 2)

Guest blogger Ryan Den Rooijen returns to complete his two-part series on Unicorn Farming.

It’s been great to see the conversations that part 1 has sparked on social media. It’s always encouraging to hear other experienced analytics leaders, like Martin Squires, agree with many of the key points & share their insights.

Hopefully you find part 2 as interesting and useful. I’m tempted to make a “neigh” joke, but I’ll resist. Here is part two, with a focus on how to assess candidates background & experience.

Casting your net wide enough

As we reviewed in part one, while there is much consternation about the current state of recruitment in data and analytics, the challenge is less than perceived.

The world is changing slowly enough that companies have ample time to build the necessary capabilities to adapt, should they choose to. Additionally, due to the fungible nature of many technologies that underpin these changes, there are multiple paths to developing the appropriate capabilities. In this article we will cover one approach to successfully doing so, through investing in talent.

Firstly, you need to identify the right people. This means casting a wide net — both inside and outside the organisation — and keeping an open mind about the type of candidate profiles you want to hire. This can include forgoing traditional role requirements, such as academic degrees.

Many leading organisation have already stepped away from this criterion. Not only does dropping academic prerequisites increase the candidate pool, but this can be strongly beneficial for diversity as well. For example, while requiring an A level (UK high school) maths degree might seem reasonable for analytical roles, this will automatically favour male applicants at this time.

Is a horse close enough to a unicorn? Just add carrot & glue?

Secondly, no matter how strong a candidate’s background, rarely will they already possess the perfect balance of technical skills and business acumen.

This is partly driven by the differing technology landscapes and cultures across organisations. This means that investing in the right training is crucial to building capabilities and allowing people to be their most effective. Classroom–based training is only one part of this. Whether someone is reviewing code on Stack Overflow, pair programming with a colleague, following an online training on Udacity — all of these are valid routes too.

Thirdly, ensure you can articulate the value that a candidate will be adding. Given the demand for analytical talent, it is baffling how many organisations have not given enough thought to why they need these capabilities.

Nothing is more demoralising than starting a new job before realising that neither your manager nor your team is sure what exactly you are supposed to accomplish. Conversely, if you are able to create a team and environment with a crystal clear mission, where everybody understands their purpose, this can provide an irresistible lure to even the most seasoned unicorn. Meaning beats money.

Getting recruitment right takes effort & focus

Finally, are you investing enough in recruitment? With unemployment at an all-time low in the industry, candidate experience is more important than ever. Are you reviewing resumes as they come in? Are you making time to schedule interviews? Are you providing feedback in a timely fashion?

It can be easy for candidates to fall between the cracks. You cannot always guarantee that your organisation is going to provide the most exciting opportunity for candidates or the most competitive package. However, you can stand out by being responsive, thoughtful, and moving at pace. This tells both internal and external candidates that you are serious about identifying talent.

There are no shortcuts when it comes to attracting the right people and building out your teams — short of acqui-hiring a suitable company! Building capabilities takes time, particularly when you are looking to attract a unicorn generalist team to span both the business and technical domains. However, by adhering to these principles you can greatly increase your chances of success.

How are you building capabilities in Data Science?

Thanks again to Ryan for such an eloquent series packed with practical tips from his experience.

How is your current approach to recruitment & development? Is there anything that needs improving in how you are building capabilities? It might help you to pause now & write down one thing you will do differently as a result of reading this series.

Putting one change into action soon is often the most pragmatic way to benefit from such advice rich posts. I wish you well & hope your future job candidates benefit.