Are there four different types of Data Scientist?
September 14, 2014
Part of the field of predictive analytics has recently been thrust into the spotlight under a new name: Big Data. As specialist recruiters in this fast developing sector, we often fulfil an educational role with our clients – helping them define role requirements and understanding what sort of candidate would fit their business. However, we always come up against the same problem. There aren’t many quality Data Scientists out there who are available…..
For those in related sectors who are interested in a change, I will be writing two initial blogs to give you some more insight into this fascinating industry. This blog will focus on the four different types of Data Scientist – as defined in the fascinating “Analysing The Analysers” report (see below), and the next one will explore typical routes into the industry and the qualifications required.
Data Business people have their eyes firmly fixed on the bottom line. They are focused on the organizational aspects of Big Data and its wider impact on their business. They are most likely to have had team management experience, many may previously have been entrepreneurs, and they are often MBA grads. However, they still have significant technical skills, with engineering or related degrees. Apart from the management, they will still get involved in data analysis activities.
Data Creatives have the broadest skillset and drive the direction of projects as they have visibility (and understanding) of the entire process. From extracting the data, integrating and layering it, to performing the required statistical work, to visualizing and interpreting it and then drawing the suitable conclusions for the business…. they can do it all. They could be seen as the “hackers” of the industry and have the most OSS experience.
Data Developers are the technical gurus. They understand how to get the data, how best to store it and how to learn from it. A lot are experts in coding and machine learning, with many coming from a computer science background. They have fluency in more programming languages than you have fingers and toes.
Data Researchers are the (mostly) Ph.D. qualified statistical wizards. Many data scientists start with academic research in social sciences or statistics, and deep academic training is vital to understand the complex processes within Big Data. This group of people provide the scientific rigour behind the work streams. Nearly 75% of Data Researchers have published in peer-reviewed journals and over half have a Ph.D.
The ways in which these four types of data scientist deploy their skills are also different as you can see by the diagram below:
So, for an organization looking to increase its analytical team, firstly have a think about the skillsets of those that currently work for you. Could they perform some of these roles? Could you bring someone senior in to coach them and grow your team organically?
If you do need to go into the recruitment market, ensure that you are working with a recruiter that understands the differences. Data Scientists are not all equal – make sure that you find the right blend for your business needs.
Source: “Analysing The Analysers.”