There is a lot of positivity around hiring now, after the seemingly endless postponement during the Covid-19 pandemic. Well, some companies are hiring, while others are playing their own waiting game. They are holding out for their ‘dream’ candidate. A candidate who may not exist. So we ask, how can you hire well without feeling like you compromised?
It is really about how long are they willing to wait and if the wait is actually damaging their business. Data science is becoming more and more of a niche and as it segments more and more, the likelihood of finding that ‘perfect’ candidate becomes harder and harder.
Some see positives in hiring and investing in their new candidates and that is wise. If you have a long list of ‘tick boxes’, you will likely never settle for anyone, so you must be aware of the risk of this and hone in on the key attributes you need from the candidate. It can be expensive to hire the wrong prospect but it is equally as expensive to hold off on hiring, causing teams to fall behind on projects.
Keywords can be tripping you up in your search. You may have keywords like AI, Machine Learning, etc. on your list and the candidates’ CVs have those words on them but when you reach interview stage, it becomes clear that the candidate has only tangential experience of those disciplines. Reading data science resumes is a whole skill in itself as there are so many terms and cross-overs to wade through. Understanding and analysing the candidate’s experience is paramount.
With that understanding, perhaps from experience you bring in to help, you can make a much more streamlined and relevant list to tick off. If your forte is not data science or data science recruitment, it is unlikely that you will know how to approach this, and there is no shame in it either, you have your own skills and experiences.
Posting job specs that do not correspond to the actual role, is a common occurrence these days. With that sort of thing happening, it is no surprise that the wrong candidates are being called into interview, are rejected for the role, time and resources are wasted and the process becomes interminable. Psychologically, it can feel like an impossible task. The mistakenly interchangeable terms of data scientist, data wrangler and data analyst can lead you down a blind alley. With understanding comes respite.
There are many qualified individuals out there, who may have been too busy to update their knowledge base. If an experienced data scientist has been busy working on largescale projects, they would not have found the time to undertake further learning to gain a certificate. On the flipside, a junior data scientist would have had the time to do so, yet would not have the requisite experience on largescale projects. If you going by the tick box method, you may select the resume of the junior without appreciating that they are the junior. Of course, juniors are great for a role where they will learn and grow, but for leading a data project your future business requires to be a success, they will be out of their depth.
Do not give in to desperation, as that will result in the wrong candidate being hired. You do not want the wrong candidate and you also need to give up your search for your ‘dream’ candidate. ‘Dream’ candidates are like ‘Dream’ houses. They are a figment of your imagination. But that does not mean you cannot find a nearly perfect house, work on it a little, spend a bit of money, and create one that is closer to what you wanted when you started the search.
Finding the ‘Dream’ candidate is a tough ask but a specialist recruiter will be able to find the candidate that most closely matches your list.
Curious about how Zenshin Talent can help your organisation? Contact us today for a no-strings conversation about your needs and our experience.