The role of the Data Scientist is changing
As organisations have started to realise the importance and value of data, their need for the right skills and expertise has also increased. In trying to make sense of the vast amount of data gathered on a daily basis and use it to solve business problems, identify insights and trends and make decisions to support new ideas, they need more people with a mix of statistics, database, data visualisation, machine-learning, coding and data preparation skills.
Data scientists are increasingly in demand to steer information and technology strategy, of which AI is a key element. As hiring them becomes a board-level priority, how is the role evolving?
Increased specialisation
As the overall level of data literacy improves across the workforce, with other employees gaining a better understanding of how to use data more widely in their own roles (such as marketing intelligence, employee retention and absenteeism figures), there will be a shift towards the future data scientist becoming more specialised. As a result, some organisations may need to consider skill-specific data scientists.
An emphasis on problem solving
Data scientists need a much wider skillset basis than statistics or data analysis; data scientists often need to start by creatively looking at the problem and understanding how to resolve it in order to achieve business goals. As a result, expectations are growing that data scientists will bring domain expertise, communication skills and an innovative mindset to the table as well.
AI knowledge more important than ever
Machines are much more able to cope with complex calculations than humans, which in turn has led to many organisations looking to AI, supported by professionals that perfect the algorithms and communicate how to build them into the overall business strategy. Deep familiarity with artificial intelligence is becoming a ‘must-have’ for many data science positions. Keeping up to date with the latest developments and research into AI is a must, if only to understand when ‘industry standard’ versions can be adapted to work better for the company.
Higher expectations
Businesses now recognise the importance of data science, but increased demand and investment means they expect people who are confident and able to align a data strategy from the off. While at one point, data science was a niche specialism, as it now informs and feeds departments across the company, it’s expected that analysis experts will also be competent in all related software, business reporting and machine learning.
More private-sector opportunities
Many data scientists are currently hired by companies that have little or no understanding of the discipline, often joining a very new department. It’s estimated that data scientists currently spend on average less than 20% of their work hours doing actual science and analysis and digging for deep insights. As the private sector comes to realise the value of data science, it could well be that focus on return on investment and profit could drive a new wave of efficiency, using data science to increase productivity.
More focus on strategic solutions
While the sector grows in importance, it does mean that competent practitioners are becoming more influential in the business world. While most positions will still require that a data scientist knows their way around programming languages, in some organisations, data engineers are taking on more of the data preparation, leaving data scientists to expand their roles to identifying opportunities where they can help grow the business. This broader scope could see their priorities being incorporated as part of wider business aims or goals.
Data Science roles often remain unfilled for up to 45 days, due to skills shortages, according to IBM. The University of York’s 100% online Computer Science with Data Analytics Masters is aiming to change this. The programme is designed specifically for those who may not currently have a computer science background but want to launch a career in this in-demand field. The course covers a wide range of topics such as algorithms and data structure, machine learning, software engineering and artificial intelligence, preparing graduates for a wide range of positions.
As the programme is delivered 100% online, there’s no need for campus visits. You can choose from six start dates per year, enabling you to remain in your current position and work around family and other commitments. There is the option to pay-per-module, and some students may be eligible for a government-backed postgraduate loan to cover the cost of the course.