The answer to recruit the best data scientist for employers

Aug 16, 2018 | 5907 Views

For something that hasn't been around for too long, data scientists have taken the right steps towards greatness. It was declared as the "sexiest job of the 21st century," by Harvard Business Review. While the job has got a great description by Harvard the career path for now is a bit uncertain. For the employers it has become the race to how can give that competitive advantage over their competitions, this is becoming a challenge for many organisations as many data scientists fail to complete the tasks and meet the organisations expectations. Monetizing their data and getting as much as they can from analytics is now critical for increasingly data-driven organisations. According to a survey it is how the data scientists are placed in an organisation is what the cause of the problem is. Here's how an employer can recruit a deal breaking team of data scientists.

Cost

Someone with experience and a Doctorate in machine learning is going to demand a high salary and will likely be looking for uplift in earnings when they move. Organisations like start-ups or small businesses, 
suffer the most from this, for them this makes hiring the right talent on a full-time basis prohibitively expensive.

Time. 

Finding the right candidate for a specific role is challenging. When it comes to hiring data scientists it can be almost impossible. With a huge shortage of qualified data scientists, many hiring managers are already struggling to find any candidates, let alone individuals with both the right skill set and mindset. In a competitive field like data science, strong candidates often receive three or more offers, so recruitment success rates are commonly low. This can make hiring them hugely time intensive.

Skill set

Mistakes are costly and, sadly, very common. Data science as a discipline has not been around for that long, and there are huge disparities between the ways data scientists are deployed across organisations. For this reason, job titles don't always tally, which can make it hugely challenging for recruiters to identify candidates with the right skills. The level of investment in and responsibilities of the data science team will also vary hugely from organisation to organisation. Some organisations are yet to appoint a data science team, while some are well on their way to becoming a real-time, data led-organisation. This disparity means that when it comes to hiring data science talent, organisations are likely to be presented with candidates who have variable experiences, contributing to the difficulties many face in appointing the right individual.

Holding

Not only is hiring a data scientist a challenge, but holding is a huge issue. With so many data science jobs out there and so few data scientists to fill them, it is not surprising that specialists in this area respond to demand for their skills. A recent study by Stack Overflow found that machine-learning specialists topped its list of developers who said they were looking for a new job (14.3%), and data scientists were a close second (13.2%). This means that if you have full-time data scientists on your payroll, making sure they stay with you can be a huge challenge.

Type of employment

Traditional hiring practices are overly focused on individuals. When it comes to data science, recruiters may identify one data science superstar; but one person will only ever be able to achieve so much. If you do recruit one or even two data scientists to work within your business on a full-time basis, their skill set will have to match the specific areas of focus within your organisation and these are, naturally, likely to change over time. If their specialism doesn't match the business need then they may only be able to tackle 10% of the problems the business may have.

Return on investment

After recruiting, the organisation should to be careful and thoughtful in managing the resource. There will be an induction process and training, which can take weeks or longer. By outsourcing a team for a specific problem, it removes the hassle of recruiting internally. Additionally, when that team starts they are put to work immediately on solving problems so often achieve results much faster. Unlocking the power of an organisation's data can be completely game-changing. We know first-hand that data analytics is often the "secret sauce" in organisational success. But trying to recruit a data scientist or team by using traditional recruitment methods can cause frustration on both sides.

Source: HOB