Within its relatively recent lifespan, data science has emerged from the drawing boards of academic circles into the forefront of modern-day business innovation.
With the ability to generate real business value, leading organisations are taking on board scores of data scientists to extract and interpret data.
But before you start luring recent Cambridge physics graduates into your office, it’s essential that you establish a strong business need – otherwise your data science function could run the risk of malfunctioning.
Here’s some advice on how to incorporate a data science function into your organisation:
What does a ‘normal’ data science team look like?
The ideal structure and set up of a data science team largely depends on their function within your organisation.
Sit down with your senior leaders and map out a strong foundation of ideas with clear objectives and a well-established business need – these demands will dictate your data science team’s overriding structure.
For instance, what do you want your data science team to achieve – and what data will they need access to? Will they assist with your existing engineering or product development teams? Will they be client-facing or business-facing?
It’s also worth noting that, before you hire a data scientist to start working their magic, you’ll need to have the right groundwork in place first - they’ll need high-quality and accessible data to stand on. In most cases, this means you’ll need someone who specialises in collecting, cleaning, categorising and storing data, such as Data Engineer.
Once you have the right foundation in place and a few data scientists on board, you’ll also need to find the right person to head up the team. For smaller organisations or teams, a Lead Data Scientist is ideal – preferably someone who is very hands on and can play an active role in the day-to-day running of the team. As for larger organisations or teams, a Head of Data Science with a strong commercial and strategic background (as well as a strong technical understanding) would be a better fit.
A typical data science team might also include a Product Developer to work collaboratively on new products or features – and, particularly for smaller organisations, it’s not uncommon for data analysts to work alongside data scientists, carrying out some of the more lightweight tasks.
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