Candidates working in data science are extremely popular right now and will continue to be for the foreseeable future. What does the role entail and what are the key skills involved?
A data scientist is required to handle vast amounts of unstructured data, which is one of the ways in which the role is distinguishable from that of a data analyst. This data comes from a number of sources, and a data scientist will then produce solutions which they can deliver to the business. They do this using algorithms, artificial intelligence and machine learning among other methods.
As James Milligan discussed in his blog on the most in-demand tech jobs for 2022, organisations are looking for people who are going to come in, extract data and then offer insights so that the business can take action.
The most useful skills a data scientist can have really depends on the role. We can split the roles into three core pillars:
Analytical – a solid grasp of mathematics is a must, while a degree or PHD in computer science, statistics or engineering is strongly preferred. Data scientists will be using analytics tools, so proficiency with these will be useful. Examples include SAS, Hadoop, Hive, Apache Zeppelin, Jupyter Notebooks and Pig among others.
Technical – the ability to use the aforementioned analytics tools will be important. As well as that, an ideal candidate will be fluent (or at least proficient) in programming languages such as Python, R, SQL, Perl (5) and C/C++. This is also where an understanding of artificial intelligence and machine learning will matter when processing the data.
Commercial – this pillar is more distinct and, although there is some overlap, requires a different set of skills. A working knowledge of the relevant industry is valuable, as is realising the ways in which the data and insights will be used. While not irrelevant in the former two pillars, soft skills are of higher importance here – candidates will have a greater business acumen and ability to communicate.
As mentioned above, possessing a degree in mathematics or statistics is highly advantageous, while a higher degree in a related field is no bad thing. Besides these, I’d recommend having some form of experience in analytics or scientific dissertations, particularly if it has entailed working with unstructured data.
Employers will be looking for candidates with an ability to code, so learning to write any of the languages listed above would be a good start. The hiring party may want to see some evidence of this, which means applicants should be prepared to present.
Beyond this, there are certain soft skills that prospective data scientists will have mastered in a previous role, or even while in education. I’d highlight critical thinking, complex problem solving, risk analysis and having worked as part of a team.
As recently as a few years ago, we would see companies hire a data scientist without any real strategy of how to implement them. As the tech industry has boomed, we’re now seeing that organisations are better informed/well prepared regarding their data strategy. As a result, they have a much clearer idea of the role that a data scientist can perform for them.
Of course, with that has come an improvement in the technology available to these organisations. Most platforms work in a certain way, but a good data scientist will be able to adapt to advancement - change is pivotal to the role.
Find your next role in data science here.
Mark Standen Director, Hays Enterprise Technology Intelligent Automation - UK and Ireland
Mark is the Director of Hays Enterprise Technology’s Intelligent Automation practice. Mark has 20 years in Technology Staffing and six of these have been in driving Intelligent Automation services and solutions from both a consultancy and talent perspective.
Martin Pardey Senior Business Director Hays Technology – South East UK
Martin Pardey is a Director for Hays Technology in the South East, the World’s largest specialist recruitment company. Martin has 15 years’ experience at Hays, specialising in the last few years in the Business Intelligence and Data Analytics sector.
