In our new report, Dynamics of data science skills, we recognise that there needs to be a focus on developing people like you, with the knowledge to make sense of the volume and complexity of data.

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As a data scientist you will have a mix of technical skills and inquisitiveness. You will know that data science is revolutionising how we understand the world, and that your skills are highly sought after across academia, industry and the public sector.

But are you also aware of concerns that large multinationals with the resources and appetite to absorb data science talent are doing so at a rapid pace? Or that this could have a knock-on effect on small and medium size businesses, charities, universities and the public sector that struggle to recruit and retain talented people?

In our new report, Dynamics of data science skills, we recognise that there needs to be a focus on developing people like you, with the knowledge to make sense of the volume and complexity of data.

We also suggest ways to better distribute your skills effectively across all sectors.

Employers across the UK are advertising more data jobs than ever

To better understand the demand for data scientists, the Royal Society applied a classification system developed by Burning Glass Technologies – an analytics software company providing real-time data on job growth and skills.

We found that between January 2013 and July 2018, the number of job adverts for Data Scientists and Advanced Analysts (DSAAs) increased by 231%, from around 8,000 to more than 27,000. This was driven by an explosion in listings for Data Scientists (+1,287%) and a major upsurge in listings for Data Engineers (+452%).

We know that definitions have evolved, partly as a reflection of changes in technology and data handling, and so job titles are inconsistent and may have changed in their use over time. To fill in the gaps, we also looked at the types of skills that are increasingly sought after in DSAA jobs. In 2017/18, this was Data Science, Scripting Languages and Big Data.

Data science disrupts traditional career structures

Data science is no longer the preserve of highly technical STEM- or finance-oriented roles. It calls for new combinations of capabilities and more collaborative, interdisciplinary ways of working. It challenges traditional career-building and presents opportunities for people from a variety of backgrounds.

In order for your skills to be fully utilised across all sectors, there are a number of things that we have recommended to enable the movement of data science skills. This includes better understanding the drivers and blockers that lead people to different roles or that enable or inhibit movement between sectors.

Drivers, blockers and recommendations for enabling movement across sectors

Academia: Within universities there are high levels of expertise and access to very talented people. Fellowships and awards create opportunities for researchers to build their careers, and there are growing opportunities for spin-outs and joint appointments with industry. However, career paths can be uncertain with a lack of permanent positions. Non-standard research outputs (eg code) are not always valued.

Industry: In companies, salary is a big pull alongside access to real-time data and high-levels of computing power. But research and development is subject to demand, publishing is often not possible (due to IP rules) and there can be a lack of understanding of the value and limitations of data science.

The public sector: In government, combining data with analytics can allow a better understanding of population needs. Applications of data science can be directly beneficial, eg to improve healthcare and treatment discovery or improve the efficiency of public services. But public sector bodies struggle to match market rate salaries to bring in the right talent and skills.

In our report, we recommend that to retain talented staff, more should be done to better recognise the outputs of data scientists and engineers, and the benefits of creating cross-disciplinary data science teams. We also recommend that data scientists in industry could benefit from collaborating with university, and vice versa. For the public sector, we suggest that it could usefully consider how to widen access to its data, including sharing data and data challenges, to researchers.

Across all sectors, there is a real intellectual curiosity around using data and the tools, techniques, algorithms of how data can be used more effectively. We encourage you to read our collection of 15 personal stories (PDF) of data professionals who have had careers spanning sectors, to find out more about how movement can work in practice.

Sharing talent sustainably

If you have been inspired to consider a joint career or donate your skills, there are a number of ways to make moving between sectors a natural part of the data science career path. Mechanisms exist that can help you donate your knowledge and skills whilst broadening your own experience.

For example, joint positions between sectors have an important role to play in sharing talent sustainably, and in promoting diversity of experience. Models such as collaborative events and partnerships, data institutes and accessible data stores bring data scientists and data together. This can be invaluable for organisations that do not have the resources to hire data scientists.

We champion the view that moving between sectors is not only possible but an advantage for data scientists and the organisations that they work in.

We encourage you to read our collection of models and mechanisms (PDF) and consider how they might enrich your own career.


  • Jennifer Panting

    Jennifer Panting