Role of the Data Scientist in the B2B Era

In businesses everywhere, the digital transformation is spawning a bunch of new job titles. Among them are Chief Data Officer, Big Data Architect and Data Visualizer. All these sought-after specialist data roles are having a major impact on the workplace.

By Jean-Cyril Schütterlé, VP Product and Data science at Sidetrade.

The task of the Data Scientist is to come up with innovative algorithms that deliver actionable business outcomes from volumes of unstructured data. It’s this that’s driving their growing prominence. Companies are now grasping just how crucial large-scale data analysis is for anticipation and decision-making. But what does the hazy title ‘Data Scientist’ mean exactly? What are the core skills the role requires? And why is this kind of talent in such phenomenal demand? Here’s our spotlight on the data scientists of the IT world.

In businesses everywhere, the digital transformation is spawning a bunch of new job titles. Among them are Chief Data Officer, Big Data Architect and Data Visualizer. All these sought-after specialist data roles are having a major impact on the workplace.

The number of Data Scientist roles the US has to fill has increased by 15,000%. That’s according to the latest employment trend data published by Indeed.com. In France, it’s a similar picture. The government predicts that Big Data field will generate 10,000 new direct jobs by 2018.

  Data Science straddles the domains of machine learning and data engineering. It unites a broad subject field. It can be applied simply to the automation of product recommendations for e-commerce. Or employed at a more complex level to autonomous vehicle technology.

But the Data Scientist’s technical tool set alone is not enough. It must be supplemented by a broader skill base. One that includes a solid understanding of the challenges that today’s businesses face andnd an ability to communicate at all company levels. The culling of data to extract genuine added business value has made his new role uniquely strategic.

The Data Scientist must be up to the task of identifying his target areas first. Then he should  use his fine-tuned grasp of data to create an ‘in vitro’ algorithm that’ll work ‘in vivo’. “The purpose behind the algorithm is almost more important than the algorithm itself”, says Philippe Very, Lead Data Scientist at Sidetrade.

Data Scientists help end-users get results. To achieve this, a three-step logical approach is required. The first is understanding the algorithm. The second is the deployment of computer science. The third is determining the right method to realize the end goal.