By By Scott Carey
Aug. 4, 2016
With the rise of big data comes the need for more highly skilled people to mine and interpret that data for businesses. This is the role of a data scientist, the job that Harvard Business Review called "the sexiest job of the 21st century" back in 2012.
With more and more tech companies looking to make sense of their customer data, and with salaries topping out at £100,000 you can see why graduates with quantitative degrees - think mathematics, computer science, astrophysics - are becoming data scientists.
What is a data scientist?
A data scientist's role is to derive actionable insights from huge data sets. This is different to a data engineer, whose primary role is to store and prepare that data, so someone with expertise setting up and maintaining large databases. The skills required of a data engineer tend to be more technical, with knowledge of Hadoop, SQL and NoSQL databases.
"Data engineers build massive reservoirs for big data," says Sophie Adelman, head of sales EMEA for Hired.com. "They develop, construct, test and maintain architectures such as databases and large-scale data processing systems. Once continuous pipelines are installed to - and from - these huge "pools" of filtered information, data scientists can pull relevant data sets for their analyses."
Data scientist: Qualifications and skills
Adelman says a strong undergraduate degree in 'quantitive' subjects such as mathematics, economics, finance or statistics is key. "You do see a lot of PhD and Masters coming out, as data science is a good way to apply what they have learned," she says.
In terms of hard skills, a data scientist will be expected to know how to interact with, and query, a database, so knowledge of analysis and data modelling skills such as Apache Hive and Pig and programming languages Python and R are useful.
However, Adelman from Hired says it is the soft skills that many candidates fall down on.
"Technical skills are important but people tend to over emphasise that. Commercial experience and soft skills are equally important," she says.
"If people don't have the ability to understand what the problem they are trying to solve is and communicate it in a way that others can understand, it is difficult to be a great data scientist."
Her advice? Go in search of more applied experience if you are a student or recent graduate. "You could use Kaggle to get more real world experience and learn new techniques, or work as a financial analyst in banking, or take Coursera courses, or take on research projects."
Nuno Castro, director of data science at Expedia also sees business awareness as an important skill. "People who can understand the organisation from a commercial perspective, and who create relevant relationships in the organisation will be more successful," he says.