It’s funny how new terms are coined and new idioms become commonplace. Like ‘data scientist’, for instance.
In the ’80s, we grew up with Opal Fruits and Marathons – the millennials among us might mistake these for new programming languages, but the more mature audience will know they are mistaken.
Conversely, if you had told me a few years ago that you were a data scientist I might have stared at you agog. Today, I will begin questioning you at length about who you work for, what statistical packages you have had exposure to, and whether you see yourself as more of a data miner or business-facing data solutions expert.
I had the pleasure of attending Crème Global’s Predict data conference in the RDS last week.
In addition to enjoying a number of excellent speakers, I was delighted to hear an expert from SAP describe a shift that reflects my own observations from the three-and-a-half years I’ve worked in business intelligence and data analytics recruitment.
What he addressed involved the shift in data from business intelligence (analysis, reporting and dashboards) to the era of advanced analytics (predictive, deep dive, data modelling).
Shift in recruitment patterns
This spoke to my own experience in data recruitment. When I started out, key requirements were for business intelligence (BI) developers, and analysts with a range of different reporting tools (SSRS, MicroStrategy, QlikView, etc.).
In the last year, demand for these skills remains high, but the demand for data modellers, predictive analysts with strong mathematical, statistical, and physics backgrounds, and people skilled in SAS, SPSS, R, Python and other open-source technologies has been relentless.
BI-reporting tools will remain important if the democratisation of data – another key theme of Predict – is to happen.
The democratisation of data refers to data being usable by, and accessible to, everyone – something akin to self-service business intelligence.
The shift to advanced analytics has been driven by organisations understanding that data has exponential commercial value in predicting future (and analysing current) consumer behaviour patterns. This is applicable across nearly every industry but is most pronounced in the banking, financial services, insurance, pharma, telco and FMCG sectors.
Data science — the growing trends
The term data scientist was coined in 2008 and, at Predict, LinkedIn reported a 36pc increase in data scientist profiles in the last two years.
A number of key data scientist trends were also highlighted by LinkedIn at Predict.
Data scientists all have strong educational backgrounds, with 34pc educated to PhD level. Their backgrounds span science, maths and physics, and one in two comes from a research background.
These figures very much mirror the type of candidate data science employers have been seeking out in the last year, which shows a definite shift to more academic profiles than were previously sought.
In addition to the increased demand for more statistically focused predictive modellers, we at Hays Recruitment have also seen a continued focus on business-facing roles, such as data interpreter.
A data interpreter is usually someone from a business or IT background, who is used to communicating between business and IT, and understands and communicates well the needs of both. Typical roles here would be data strategy or data solutions experts.
An issue of diversity
Hays Recruitment recently reported on the dearth of women in IT. We found that counteracting the lack of female role models, as well as creating better IT education, was one way to address the skills shortage and encourage women into IT.
Data science offers an incredible opportunity to combat this gender gap. Now is the time to communicate the variety of data roles, and their suitability for diverse personalities and skillsets.
In this way, we will encourage a much more diverse set of people, women and men, to become the data scientists of the future.
Anne-Marie Walsh is a managing consultant with Hays Recruitment, specialising in senior data appointments and account management.
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