female data scientist, woman leading team

The world needs more data scientists

female data scientist, woman leading team

Dr Anya Rumyantseva, Senior Data Scientist at Hitachi Vantara

Data science is often referred to as a ‘dark art’.

As a data scientist myself, I don’t think the field is that mystifying. But for those outside of the profession, there is some lack of awareness of what a data scientist actually does, and what pursuing a career in the field entails.

This can be a real problem – because today, data makes the world go around.

Most companies, regardless of industry, are seeking new ways to leverage the vast amounts of data at their fingertips as a tool to drive efficiencies and transform their business model. But like any tool, data is only useful if it’s in the hands of someone who knows how to use it. It’s easy to forget that digital transformation is as much about people as it is about technology.

The talent deficit 

The UK has been struggling with a skills shortage for some time now. As digital transformation influences every sector, businesses are turning to experts who can help them harness their data. Companies are on the hunt for data engineers, machine learning engineers and data scientists. One study found that in the UK, the demand for people with specialist data skills has more than tripled over the past five years, while another projected the data scientist role will account for 28 per cent of all digital jobs by next year.

It’s a case of supply and demand – but unfortunately, many companies are encountering a sparse talent pool to recruit from. Some estimates even suggest that Europe needs around 346,000 more people trained in data science by 2020. That’s a big gap to fill – and it’s only going to get wider unless the industry takes action.

The data landscape is getting increasingly complex – how much data we’re generating, the types of data and how we’re storing it is changing. To put this in perspective: I’m working on a project right now that uses a petabyte of data. I’m able to work with this huge amount of data because today we have the infrastructure to store it, process it and apply machine learning models. Rewind to the 80s and it would have cost around $600 billion just to store that much data.

Now that we have the tools to work with such large data sets, we’re able to leverage data in exciting new ways. However, this also means we need more people capable of doing so. Considering that IDC forecasts a massive 163 zettabytes of data will be generated by businesses every year by 2025, it’s no wonder UK businesses are worried about a deficit in data specialists.

So, how do we mitigate an impending skills shortage? Well, a good place to start is by changing perceptions of what a data scientist actually is and what they do.

Demystifying the ‘dark arts’

I’ve been a data scientist in Hitachi Vantara’s Solution Engineering team for over two years now. When people ask me what I do, the answer may not be what they expect. My role is to understand the business challenges of our customers, consider potential analytical approaches to solving these challenges and prototype solutions by using advanced analytics, machine learning and deep learning techniques.

In short, I leverage data and mathematical techniques to solve business problems. It’s an exciting field to work in – and can have a significant real-world impact.

As an example, consider the UK rail system. It’s one of the busiest in the world, ferrying thousands of people from point A to B every single day. When you’re a passenger, you probably don’t think about the intricate and nuanced system that keeps your train running. That is, until something goes wrong. Like when a train door gets jammed and is prevented from leaving the station on time. One seemingly minor fault can have a huge knock-on effect further down the line, causing delays and disruption for thousands of passengers.

That’s one real-world problem that I’m trying to help to solve right now. Leveraging data collected from thousands of sensors on the trains themselves and working directly with rail engineers, as a data scientist on the project I bridge the gap between engineering and mathematics, uncovering insights that can drive efficiencies and reduce delays.

Diversity matters

Hopefully now you’ll think of a data scientist as more than just someone who sits behind a computer screen doing equations all day! But the tech sector needs to work hard to build a more inclusive environment where young people – regardless of their background, gender or race – consider data science as an attractive career option.

At Hitachi Vantara, we run a data science internship programme in our London office for talented and intellectually curious young people from diverse backgrounds. Our interns roll up their sleeves and get stuck into analytical projects. They are an important part of the team and their opinions matter. We challenge them to think creatively, asking them to leverage publicly available data to uncover insights into real-world problems – like using data from the Department of Transport to think up new ways to reduce carbon emissions from private and commercial vehicles in the UK. It’s not just a fun thought-experiment – it’s an accurate glimpse into the life of a data scientist.

Data science is a diverse, interesting and constantly evolving field – so it needs people who can think differently, bring new ideas and offer fresh perspectives. If we’re going to tackle the skills shortage, the industry must hold the door open for people from all walks of life.

Anya Rumyantseva, Senior Data Scientist, Hitachi VantaraAbout the author

Anya Rumyantseva is a Senior Data Scientist at Hitachi Vantara. Anya received a Ph.D. degree from the University of Southampton and BS/MS degree in Physics from Lomonosov Moscow State University. Anya is also a fellow of the Nippon Foundation (Japan). Her PhD thesis was focused on using IoT data obtained from marine robotic systems for improving our understanding of phytoplankton blooms and their impact on the global climate. At Hitachi Vantara, Anya is working on projects that use advanced analysis and machine learning techniques to improve business operations in the railway, manufacturing and other industries traditional for Hitachi group.