The Magnificent Six

Data Science has become a very competitive field in the past few years and this year it seems that that competitiveness has reached a peak. As we know the job market has flipped to being candidate-led, and really specialised roles like that of Data Scientist are a struggle to fill.

There are a lot of candidates leaving universities with a good amount of skills but those who are most in demand have a little bit more experience under their belts and a skillset made up of seemingly disparate elements which make sense within the context of the jobs on the market.

Not only do they need to become data experts but there is also a need for them to develop development skills too. To remain competitive you should look at the following:


Speeding up the processing on projects is a really important element of the team working cohesively. ML and its lack of proficient industrialisation can hold back data scientists. They must work together with the IT team to ensure that the cloud is optimised and the computing rates are as fast as possible. Moving the correct parts to the cloud aids in the ability for team members to work from distance, without losing the advantage that virtual servers provide.


If you aren’t already using Agile as an organisational method, you are behind a lot of teams who swear by it. It is a non-hierarchical system allowing companies to be more flexible to changes in their market. Customer-centric, it revolves around fast cycles, opens comms and autonomous teams. As more Data Scientist/ML Engineers emerge, the changing machine learning side within codebases requires agility of organisation, enabling continuous and smooth implementation.


This software makes it easier to collaborate with many developers working on the same project. Data Science becoming heavier on the development side means that something like Git starts to become a prerequisite on job specs. Whilst it is likely that you will have started using it on your own when you start training on it, it is necessary to use it with other experienced users to demonstrate that you can use it in real dev scenarios.

Teamwork, Communication and an understanding of Business

Ascertaining which problems are the most important to solve in terms of business goals in the company in which you work is a really important skill, as well as finding new ways to exploit the data you already have or can gain access to.

The higher-ups within the business will need the extremely techy information to be translated so it is  easy-to-understand, so that the info can be disseminated throughout the company to departments such as sales or marketing. This is a particular skill it is very handy for data professionals.

Data science is not usually a solitary life, as all parts of the team must work together to make the data digestible, the data is coming from other parts of the business and the results are being sent to other departments, and that is before you factor in customers. You must be a team player for it to work well.

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