Candidates seeking work as a data scientist are in high demand right now. Hays Technology’s Mark Standen and Martin Pardey explore what the role entails.
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.
Data scientist roles are set to be one of the most in-demand tech jobs this year. 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 Notebook 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.
For early-stage data scientists
As mentioned, 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.
How data science has changed
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 and 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.
By Mark Standen and Martin Pardey
Mark Standen is director of Hays Enterprise Technology’s Intelligent Automation practice in the UK and Ireland. Martin Pardey is a director for Hays Technology in the south-east of the UK. A version of this article originally appeared on the Hays Technology blog.
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