Since 2012, the number of data science roles advertised in the US has increased by more than 650% and that surge is set to continue with 11.5 million new jobs predicted by 2026. In the UK, demand for data science specialists has more than tripled over the last five years.
However, with employer demand increasing exponentially, there is a significant skills shortage. The US alone is expected to have a shortfall of 250,000 data scientists by 2024.
With the supply and demand balance tipped firmly in favour of those with these highly sought-after skills, studying the University of London’s MSc in Data Science could allow you to have your pick of some of the best jobs. But what is the reality of working in data science? We spoke to two professionals at different stages of their career about how they got to where they are and what tips they would share with the next generation.
The real work of a data scientist is to solve problems; to make the world better than when you started.
Stephen Black has a PhD in Chemistry and spent his early career working as an industrial chemist for former British chemical company ICI. He then moved into management consulting, where his interest in data began.
“A lot of the work I did involved data science and strategy. I was attempting to make calculations and forecasts for problems where you wouldn’t see the benefit for 20 years and using simple mathematics for that kind of situation just wouldn’t work. With data science you bridge the gap between your own intuition and the numbers you have.”
Stephen now runs his own data science consultancy, Black Box Data Science, and specialises in healthcare.
“The real work of a data scientist is to solve problems; to make the world better than when you started. One of the biggest areas of healthcare I’ve looked at is emergency care, specifically the target in the NHS that patients should be seen within four hours of arriving at Accident & Emergency. I was there when we met the four hour waiting time target for the first time – people did what we recommended and we achieved a result. That’s incredibly rewarding.
“I’m now working with GPs and I can say we’ve genuinely improved patient experience and the experience of GPs by building good tools and monitoring how those tools work. That’s the key attribute any good data scientist needs – a desire to solve a problem using the best and most appropriate technique.”
It isn’t the most complicated techniques that make the most impact. It’s about using a simple but appropriate technique to solve complicated problems.
The biggest mistake data scientists make, Stephen advises, is choosing technique over solution.
“I can see it’s an easy trap to fall into. Data scientists who’ve been trained in a whole range of fancy techniques have this impressive toolkit that they want to show off. But the fact is, it isn’t the most complicated techniques that make the most impact. It’s about using a simple but appropriate technique to solve complicated problems.”
Dimitrios Gousis initially studied a BSc in Computer Engineering in his native Greece before moving on to a Master’s in Data Science in the UK. As part of his dissertation he took on a 12 week project at tech firm, JustPark, where he was later offered a full time role as junior data scientist.
“At JustPark we haven’t got a huge data science team, it’s just the two of us. That means we have the opportunity to work on a real variety of projects. My role involves everything from growth strategy to pricing to customer experience, looking for patterns of behaviour from our users so we can adapt our products to better suit them.
“I really enjoy that my role is creative. A lot of my role is thinking creatively about how to make a piece of data become something really useful for the company. That’s a key skill in data science; you have to focus on what you don’t see in the data as well as what you do see.”
When asked about the key attributes of a good data scientist, both Stephen and Dimitrios agreed it was 'good intuition'.
“The big challenge you face is that solving the problem so you understand it is only half the battle,” Stephen said. “You also have to find a way to communicate that and persuade people, particularly colleagues and managers who don’t necessarily understand data or statistics.”
Dimitrios added, “You need to be flexible. There will be many times when you’ll start a project not knowing whether there will be a meaningful conclusion. You may need to adapt the approach you’re using, change the roadmap or even decide to scrap the project altogether. Flexibility is key to save your company time and money. You also need to be able to make quick decisions and learn to let your instincts guide you.”
So what advice do the professionals have for the next generation of budding data scientists?
For Stephen, the best way to stand out to an employer is demonstrating those all-important communication skills. “If you want to impress a potential employer focus on demonstrating that you know how to explain how you solve problems. Don’t try to blind them with the fancy techniques you can use, show them you can be persuasive and use your work to make a difference to whatever the company is doing.”
Don't be afraid to get your hands dirty and really dig into the data. Don’t worry if something doesn’t seem to make sense or if an analysis doesn’t conclude how you want it to, it’s all a learning curve.
Dimitrios advised future graduates to think about what they enjoy doing – whether that’s sales, finance or something else – to help narrow down their job search.
“Data science is a huge sector with many sub-sectors. Decide which path fascinates you the most and focus on applying for jobs in that area. I’d also say don’t be afraid to get your hands dirty and really dig into the data. Don’t worry if something doesn’t seem to make sense or if an analysis doesn’t conclude how you want it to, it’s all a learning curve.”
Take your pick of the most in-demand jobs in the world with an MSc in Data Science from the University of London.