Data science has become an in-demand and well paid field
What is a data scientist? The job title sprang out of nowhere as technology firms have scrambled to find people to perform sophisticated analytical tasks over the last decade.
Reports of six-figure salaries abounded as businesses competed for staff to slice and dice data in pursuit of business lessons and emerging trends.
Recruitment firm PageGroup cites annual salaries for data science work of between £60,000-150,000, while cautioning that the job title covers a huge range of disciplines and responsibilities.
The line has become blurred between data science and other tasks such as data analysis. James Hobson, a technology specialist at PageGroup notes that “there are different interpretations of what constitutes a data scientist”.
Whatever the title, demand for staff has outstripped the supply of those traditionally considered suitable to the work, usually candidates with doctorates in computer science.
A 2020 report into emerging jobs in the US by LinkedIn estimated that data science vacancies were growing at 37% a year.
Edward Green uses software to analyse motor racing data
So new entrants are coming into the field from unorthodox routes, aided by new software packages. Edward Green and Balraj Oates are two of those, although they both hesitate over the data scientist label.
For Mr Green, his data science journey began at 15 when he embarked on series of extended stays in London’s Great Ormond Street Hospital while being treated for a complex medical problem that required three bouts of surgery over two and a half years.
Most of us would prefer to forget such an ordeal. But Mr Green remembers it as his gateway to a career working with technology. “The day I had my first surgery was the day the iPad was released,” he says.
He joined the hospital’s patient council and began capturing medical data on an iPad so it could be displayed to patients. This experiment saw him head straight into IT from school.
His surgeon had worked with McLaren, studying the application of F1 pit stop techniques to the movement of patients in and out of intensive care. So motor racing – and work at McLaren’s technology centre just outside London where data from cars are analysed – was his next step.
F1 motor racing teams like McLaren generate huge amounts of data
At McLaren he uses data science software from US firm Alteryx that has developed its own self-service tool which can help people to become data experts.
For Mr Green, it trained him to juggle vast quantities of data. In McLaren’s case that means 1.5 terabytes worth collected from every race. “Sometimes the drivers feel they don’t need this data, but they do,” he says.
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Euan Davis researches the future of work at Cognizant, a technology services group. He says that perceptions of the field have changed.
“Data science used to be a very dry job. It was seen as nerdy but now it’s creative. Communication matters because you have to sell what you uncover and that means telling stories around data.”
The future belongs to people with soft skills just as much as to those who master hard data analysis, he says.
“The data science position is becoming a hybrid role. Now it’s about being a trusted advisor. The data scientist has to be able to read the data in a way that tells something important to business executives.”
Data visualisation tools, software that translates complex information into simple images, have changed the data science game, says Mr Davis: “The tools are getting easier to use and more intuitive.”
Being able to transform data into a visual format is becoming a big business
Various data analysis businesses such as Tableau and Cloudera offer this type of program, translating information into simple charts and icons for data scientists and others.
This approach recognises that not everyone is comfortable trying to extract clear information from the bewildering columns of figures that appear in large spreadsheets.
This new technology has created a grey area between the work of a data scientist and data analyst.
Traditionally a data analyst might spend more time on routine analysis and providing regular reports. A data scientist would be responsible for the way data is manipulated.
Mr Davis thinks this technology will prove reassuring in an era when “our jobs are changing around machines and we have to understand data”.
Balraj Oates moved into data science after raising children
Data science represented a dramatic change of direction for Balraj Oates.
She was introduced to the discipline via a competitive event, a hackathon where players analysed global Covid case data to create regional comparisons of the pandemic.
Alteryx software allowed her to drag and drop icons representing data sets such as death rates.
Importantly, by manipulating icons rather than pages of calculations she could match the speed of analysis of a statistician on her team. She compares working with data science tools to using a calculator.
This, she says, “started my data journey”. It propelled her back into the working world after a 12-year break raising three children.
Mrs Oates spotted the hackathon on the Women Returners website, which helps professionals returning to work after an extended career break.
She mentioned her data science experience to another mother at her children’s school who turned out to be seeking a data development specialist.
Mrs Oates now applies her new-found knowledge in the financial services industry while her eldest child studies coding.
“It’s never too late to think about developing a career and it’s more accessible than you think” Mrs Oates says, before adding how important it is to market yourself. “I got into this work through a conversation at the school gate!”