Data Science in 2018 – A Recap


Data Science in 2018 – A Recap

Data Science has really come to the forefront of conversation in 2018. Spend more than 5 minutes on any half-decent tech blog website, and chances are you’ll eventually find yourself reading something to do with Data Science or one of its related terms.

To try and go through everything that’s happened in Data Science this year would, more than likely, take me right the way through to the end of next year. But there are a few things that I think definitely bear highlighting as we prepare for the arrival of 2019.

So, what’s happened?

Much like they were in 2017 – but even more so – ‘Data Science’, ‘Artificial Intelligence’ and ‘Machine Learning’ have been massive buzzwords. An increasing number of companies across the UK have started either building a Data Science Team or expanding their existing one.

In other news, we’ve had several acquisitions this year, especially within Machine Learning and so-called AI (more on that later). Two of the most notable examples include Facebook acquiring Bloomsbury AI and Lyft recently acquiring Blue Vision Labs. If you add to that the huge Series A, B and C rounds that have come in for companies like Revolut, BenevolentAI and OakNorth, it’s easy to see this ‘AI Boom’ continuing well into the next decade.

What’s the biggest new trend?

From my own experience – and granted I only cover a small percentage of the vast market – Deep Learning in particular has benefited from a significant rise in interest. Various applications of Deep Learning – retail, autonomous vehicles, energy price prediction and more, have all entered into public discourse in a big way.

In particular, the race for Level 5 Autonomy in self-driving vehicles has heated up this year. There’s been a huge amount of investment and a number of interesting partnerships made – 2019 may not even be here yet, but already I feel confident in saying that Deep Learning will be one of the biggest if not the biggest topics of next year.

What is in demand?

The requirement for Senior Data Engineers is bigger than ever. This is really encouraging to see, as numerous companies in the past have made the mistake of hiring a Data Science team without first stopping to consider the Data Engineering tasks that need to be undertaken first, and the timescales involved with building a quality data infrastructure.

Tech-wise, development still seems to be centred mainly around Python and Scala, with the addition of various Machine Learning frameworks and big data technologies, dependent on the specific role and tech stack.

What are the challenges?

Data Science is definitely exciting, and there is a lot that having a Data Scientist can offer, for both new and more well-established businesses. However, there does also appear to be a temptation to get somewhat carried away, and many companies are starting to believe they desperately need a Data Science team when, quite frankly, they don’t – or at least, they don’t yet.

One thing I’ve observed as a Data Science recruiter is that the line between a Data Analyst and a Data Scientist still appears to be blurry. A lot of the time I spend having conversations with hiring managers is spent simply trying to ascertain whether it is in fact a Data Scientist they need. Often, it turns out that what they want isn’t a Data Scientist after all, but a Data Analyst, or in most cases, a Data Engineer. Moving forwards, it’s imperative that companies looking to make the best possible use of their data are sure they know exactly what they want before they start investing their time and money.

As a company, once you’ve established that you do in fact need a Data Scientist, acquiring a good one is often far more difficult than you might expect. Why? Because if someone really is good, the whole world will be interested in speaking to them. Making sure your company stands out with a quality infrastructure, interesting projects and a ton of interesting data is a good step towards making sure you’re attracting the best Data Science talent possible. Once you’ve got that sorted, it’s vital that you have a solid interview process prepared – one that properly evaluates the candidate, yes, but also one that sells your company as the best place for that candidate to work

As a Data Science candidate, the good news is that you’ve never been more in demand. You have the choice of almost every Data Science team in the world, and the ability to dictate what you’re paid. In fact, the biggest challenge I’ve found as a recruiter is keeping up with the so-called ‘market rate’ – in fact, I’m not even sure this exists any more.

However, as the demand for Data Scientists continues to increase, so too will the supply. Eventually, Data Scientists will find themselves having to start fighting tooth-and-nail for the best opportunities. Therefore if you’re a Junior Data Scientist or a recent graduate looking to get into Data Science, it will no longer be sufficient just to say that you’re interested in Data Science. You need to be able to show that you are obsessed with it. Whether that’s through Kaggle competitions, personal projects, or attending meetups, going above and beyond your academic qualifications is vital.

Predictions for 2019

I’m really excited by what 2019 has to offer. I’m expecting to see even more start-ups pop up, as more and more outstanding people leave the big tech firms to start their own projects. We can expect a great deal of money to be invested into these, as investors seem to be gambling massively on ‘disruptive’ firms. New innovations in fields such as Health-Tech mean that 2019 is going to provide the opportunity to work on some truly incredible projects. As smaller firms grow, a great deal of them will start building and expanding their data teams, which means more Data Science, Data Analyst and Data Engineer roles to fill.

I also imagine that the misuse of the term ‘AI’ will continue, as will the ever-increasing paranoia that robots are going to rise up and take all of our jobs. However, I’m hoping that eventually people will realise that true AI is still pretty far away – and what we do already have is still very much under human control.

With the seemingly limitless potential applications of Data Science, it’s not difficult to see why so many people and companies took notice in 2018. Companies have to be slightly careful not to get too sucked into the hype and risk missing out on other major opportunities, but there can be no denying that Data Science will continue to be a major talking point for businesses in 2019 and beyond.

Saragossa are a talent provider specialising in the Financial Technology, Financial Operations and Data Science sectors. Our role is to match clients with high calibre candidates. Our work encompasses filling temporary contracts along with building permanent teams and resourcing projects. To find out more, please contact or call 0020 7871 3666.

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