In data science rarer skills are the most in demand

The technology skills gap in data science is exacerbated by the fact that some of the hardest roles to hire are also the most niche. 

Digital Nation Australia spoke to Anna Russell, data scientist and director at Polynomial Solutions. She said that the most in demand technical skills in the data science field are niche skills around machine learning and coding languages.

“Particularly getting into the more advanced areas like deep learning image recognition, image processing, are highly in demand,” said Russell.

From a language perspective, there's a lot of demand for analysts who can code properly.

"Obviously Sequel is still very popular because a lot of companies use Sequel as their primary database language and Python and R are the two popular languages for analytics.”

While some of these skills such and coding using Python are in demand due to a large requirement for them, others such as the more advanced areas are niche skills where there is a severe lack of supply.

Depending on the sector, the type of data and analytics skills required varies.

According to Russell, technical skills like data crunching and ease using data visualisation tools like Tableau are sought after, when it comes to campaign reporting, operational reporting and product reporting.

“That really lends itself to having a strong understanding of visualisation so that can be useful for the business,” she said.

In a sector like healthcare or pharmaceuticals however, Russell stresses the importance of traditional statistical skills.

“You’re going to be looking at possibly someone who needs to have a strong statistical background, putting less emphasis on the data-vis and communication-back because you've got a high level of quant literacy in general.”

Embedding a culture of data led decision making depends on having a quantitatively orientated leader she said. Someone that is, “prepared to spend the money and invest the effort” in the infrastructure supporting data science.