By Ben Rickard, Junior Consultant
When it comes to adding skills and knowledge to your data science team, what should you be looking for?
On Monday 10th June, we hosted the Bristol R User Group’s monthly meeting in our office, with two great talks taking place before we had the chance to network over some pizza and prosecco (or whatever your tipple of choice). What the event demonstrated is that our city is teeming with tech talent, and this group of programmers was just one small sliver of it. If that’s anything to go by, Bristol is in a strong position to build its presence on the global tech stage.
R is a language that has peaked and troughed in popularity over recent years. Three years ago it featured among the top 10 programming languages based on a PYPL rating, but it has since fallen out of Tiobe’s top 20 index by virtue of Python’s rise in popularity. Both are statistics-based coding platforms that allow users to easily compute, analyse and graphically represent data. R and Python are favoured by 40 per cent and 26 per cent of current data science professionals respectively, and they represent the skills that employers are after.
As the rapid rise of technology creates new jobs and opportunities, prospective candidates are being left in a difficult position. While data science is nothing particularly new – it’s always been around, just perhaps under a different name – the skills that it requires are ever-changing. Employers are on the lookout for talent with firm foundations in data analytics, but with an academic background that features the study and use of R or Python.
Unlike many other sectors, there are a wealth of graduate opportunities within data science, but consultancy firms know that this is a long-term process of developing talent. Graduates finish their courses, often involving a Master’s or a PhD in mathematics, with the aim of going into data science, and employers know that they are in the right position to bolster their team further down the line. What companies are struggling with, is hiring experience that can come in and hit the ground running.
This is an issue that has come up time and time again in the tech sector. While there are data analysts who can transition into a more scientific role, they are now required to work with new technologies like machine learning, natural language processing and artificial intelligence. They have the experience in the field but lack the academic backing that many employers are looking for, and that now covers these new areas.
Data science is a role that spans countless sectors. Whether it’s in retail or healthcare, finance or manufacturing, you’d be hard pressed to find a single industry that wouldn’t feel the benefit of analysing data to enhance and perfect their business processes. As a result of this, demand for data professionals has rocketed by 344 per cent since 2013, fashioning a candidate-driven market, but uptake has not risen at the same rate which has created a disparity between supply and demand.
Employers are spoilt for choice when it comes to graduates, but seasoned professionals are proving harder to come by. If Bristol is anything to go by, then – as proven by the event we’ve just hosted – there are clusters of incredibly passionate individuals who have the talent and the know-how to add a huge amount to your data science teams. Whether they hail from an academic background or one of professional or personal experience, albeit in a slightly different role, not to go in search of both is narrowing the scope of your search too far, putting you at risk of missing out on the best talent available.