Back in 2012, HBR dubbed data scientist “the sexiest job of the 21st century,” and the role has continuously secured a spot on LinkedIn’s emerging jobs list ever since. The field is more important for businesses than ever before, and companies who don’t incorporate it into their organisation are at risk of falling behind their competition. Hiring a data scientist with relevant skills, experience and a strong aptitude will make an immeasurable difference to your business long term. But, with recruiting for data science talent being at a fever pitch, the big question is how to beat the fierce competition.
So your business needs a data scientist. Great, time to brief your resourcing team, but wait; What are your data science goals for the company? What do you want the successful candidate to achieve? What will they be working on? These are all questions you need to answer before you set out on your search for a data scientist.
“Data science” covers a vast and varied range of roles and the type of work required will dictate the kind of data scientist your business needs. It’s also beneficial to determine whether your requirement is ongoing or project based. Is a full-time data scientist necessary, or would it make more financial sense to contract the work as and when required? On the other side of the scale, one person might not be enough, and you may need to implement growth plans for the team over the coming months. Again, these fundamentals need to be solidified before starting your search.
Once you’ve drilled down the details of the work, it’s time to decide what type of data scientist you need. One of the easiest ways of grouping data scientist roles is by the deliverables they create. Yael Garten, Director of Siri Data Science and Engineering at Apple says, “one type of data scientist creates output for humans to consume, in the form of product and strategy recommendations. They are decision scientists. The other creates output for machines to consume like models, training data, and algorithms. They are modelling scientists.”
The skills required for the task in hand will ultimately differ depending on whether the data is being utilised by humans or machines.
This is a simplistic way of looking at your requirements, but it’s a good starting point. It may be the case that the candidate will be working on something particularly niche, in which case you will need to look for a data scientist with relevant experience in this area. Unfortunately, when it comes to data science, there’s rarely a ‘one size fits all.’
So they look great on paper and have all the relevant experience, but are they a good fit for your business? Like most roles, it’s not just experience you need to take into consideration. One of the primary skills a data scientist requires is the ability to solve problems. They need to be able to identify a problem a person or department is having, translate that problem into something that they can answer using their programming/maths skills and then convert their findings into insight someone who is unfamiliar with data science can use. Simple, eh? A great candidate should not only be able to solve these problems but be able to walk employees or stakeholders through their decision-making process and deliver a greater understanding of their insights.
If you take anything away from this article it’s this age-old saying: fail to prepare, prepare to fail. Data scientists will be able to tell a mile off if you or your recruiters don’t know the fundamentals of the job role. This can not only put quality candidates off, but it could also prove detrimental to your employer brand. Data scientists often know other data scientists so news of a poor candidate experience can travel fast.
It’s unfeasible to have recruiters with specific knowledge in all roles the business is hiring for. If outsourcing to a specialist recruitment company isn’t an option, then make sure your team are familiar with the fundamentals of the role. This includes; which tools will the candidate be using, what sort of projects will they be working on and how will their efforts impact the company. Yes, candidates will want to know about the benefits package and where the office is located, but they’ll lose interest if the recruiter can’t comfortably string a few sentences on the role.
Candidates with data science experience are in short supply, so don’t make the talent pool unnecessarily smaller. If your role description is asking for a candidate who holds a computer science degree, significant experience in a niche focus area and knowledge in multiple programming languages the chances are you won’t find anyone who ticks all your boxes.
Due to the nature of the sudden increase in demand in data science skills not all candidates will be educated in computer science. Likewise, candidates may be degree educated in a relevant subject but not have significant amounts of work experience.
To succeed in your search, you need to prioritise your requirements. Are you open to training a candidate on the job if they have the relevant qualifications? Are you willing to rule out someone from the process due to their education background even though they have been working in data science for several years? You need to let your HR/resourcing team know what mandatory requirements are and what are nice-to-haves. And if you’re still struggling to find someone after a specific amount of time you may need to look at widening your net even further.
So, hiring a data scientist isn’t going to be easy, but by knowing what you want the successful candidate to achieve and being open-minded about the level of skills and experience required for the role, you’ll make it a damn sight easier for yourself.