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The essential future skills data scientists need to drive the UK’s AI industry forward

London Tech Week (LTW) this week has demonstrated the need for future tech skills in a post-pandemic world as one of its key trends. This couples with the UK Government’s AI Strategy announced to coincide with LTW, which aims to make the UK a global AI superpower. The strategy outlines the need to invest in talent over the next 10 years to achieve this vision. Yet, when it comes to data science and engineering the continuing skill shortage has tripled in recent years. There is a clear need for new data scientists, and for existing ones to upskill.

Faculty was founded with the purpose of addressing the skills shortage that stopped many companies making the most of AI through the Faculty Fellowship programme. The programme – which has now entered its twentieth cycle – was created to train science, technology, engineering and maths postgraduate students and launch them into industry roles. Through an eight-week course, fellows learn both the ‘hard’ and ‘soft’ skills needed for a successful career in the industry. These new abilities are then put to the test via an industry placement on a real-world data science project. From easyJet to the NHS, over 200 top organisations have employed fellows through the Fellowship. 

In a recent blog LTW itself highlighted the importance of both these soft and hard skills for the tech industry. This is made even more timely, as it shows that the competencies required in data science careers have undergone something of a reboot in the past 18 months. In light of this, we’ve outlined below the capabilities we think data scientists need by drawing on our experiences in the field.

The skills data scientists need to power the UK’s AI growth

    1. Communication

So called ‘soft skills’ such as communication might not immediately be the first skill that comes to mind when thinking about AI, but it is essential to industry success. Good written and verbal communication skills help with teamwork, liaising with customers, and showing your added value rather than being seen as an anonymous cog in a machine. Many businesses do not understand the capabilities of AI and so being able to articulate and credibly highlight the benefits of data science will be important. More insight on the importance of communication can be found in our earlier blog.

    1. Maths know-how and some coding experience

As would be expected, a good base knowledge of maths is essential for AI. Probability, linear algebra, calculus and statistics all underpin data science and will be key to help make AI a reality. Getting to know Python, or other relevant programming languages, is a great step into data science. 

    1. Problem solving and scientific knowledge

Data science goes hand-in-hand with problem solving. Whether it’s attempting to solve an organisation’s problems or figuring out how to correct an algorithm gone awry, top data scientists face problems head-on armed with their robust knowledge and skills. London Tech Week recently highlighted data analytical skills as one of the top considerations when recruiting. 

    1. Collaboration

Collaboration is intrinsic to commercial data science work. The ability to work alongside others from across the team as well as with external customers is part of the daily skillset of a data scientist. Collaboration helps with brainstorming ideas, trying them out, and often returning to the drawing board to work alongside others and overcome challenges. The need for collaboration is even more important In a post-pandemic landscape where virtual working becomes part of the new normal.

    1. Ethics

Ethical considerations are vital to using AI responsibly. We believe that AI has the power to change everything and as it becomes more and more ubiquitous in our everyday lives, it is even more important that top data science talent are aware of, and consistently apply, ethical reasoning and practices. Issues of bias, privacy, and trust must all be addressed for customers to adopt responsible AI.

    1. Flexibility

Data scientists help create change across a huge variety of fields, and flexibility is a common part of the job. From healthcare to education, commerce to manufacturing, data scientists have access to a broad array of different industries with different demands but shared core skills. Flexibility as a data scientist could take the form of being responsible for the end to end process or you might find yourself working on a specific part of the process, such as working with other specialists, deployment, or modelling.

These skills outlined above are a pathway to great success, especially given that data scientist has become one of the most in-demand jobs. The transition from academia to industry requires both the hard and soft skills outlined, which is why programmes like the Faculty Fellowship are useful launchpads for career success. 


You can find out more about the Faculty Fellowship and the skills training it offers to fast-track data science careers here.

To find out more about what Faculty can do
for you and your organisation, get in touch.