In today’s data-driven business landscape, data scientists are like gold dust. Every company wants to get their hands on one! 

And for good reason – the opportunities a data scientist can bring to the table are endless, from leveraging data to unlock fresh business opportunities, to blocking extremist content from showing up in search results, to identifying cancerous cells in the human body.

But as someone who has been working with organisations in the tech space for a while now, I’ve learnt one very important lesson – timing is everything. 

An experienced data scientist could demand anywhere between £70k and £130k per annum, according to our data - but if you take one on board without laying the right groundwork first, this decision could quickly backfire. 

So, before you send out the news that you’re recruiting for a new data scientist, here’s how to tell if your organisation’s truly ready for one:

How can a data scientist help your organisation?

Equipped with a healthy mix of technical prowess and business acumen, a data scientist can pinpoint problems, share meaningful and comprehensive insights and deliver practical solutions. This could include anything from identifying new ways to increase employee productivity, to finding innovative new methods to refine the online customer journey.

To provide an example, the data experts at ASI Data Science recently worked with one of the UK’s biggest exhibition centres to find new ways of improving its crowd management techniques, boosting security and providing greater insights into increasing revenue from visitor donations. 

To track crowd movement and time spent around the tourist hotspot, which attracts more than 5 million visitors per year, the team at ASI were able to predict visitor flow by monitoring people’s Wi-Fi usage.

By combining tens of thousands of journey maps together and analysing the results, ASI then used Markov chain algorithms to create a model simulating the movement of 500 different hypothetical visitors over a fifteen minute period. The model showed the different routes people were most likely to take, and identified congestion points and key locations that were prone to overcrowding.

This meant the non-for-profit organisation could then develop and put in place more accurate crowd management processes, which lead to increased customer satisfaction and revenue.

Make sure you have the right foundations in place.

Data scientists are masters at leveraging data to optimise your organisation’s profitability and productivity - but in order for them to be truly effective, the right infrastructure needs to be in place. 

So, before you start recruiting for a data scientist, focus your attention on the foundational data first, such as growing and refining your database or tracking your customer behaviour. (This is where data warehousing and data engineering come into place.)

And, generally speaking, a data scientist shouldn’t be your first hire. They’re far more effective when there’s an engineering or analytics team already in place.  

It’s also worth considering what you’re trying to achieve or measure before you employ a data scientist. This will not only manage your organisation’s expectations, it will help the person in the role to know what they’re working towards.

Once you’ve built a solid foundation, a data scientist can come in and help you forecast future sales performance, or perform statistical interpretations, or build, test and execute predictive models.

What organisations can benefit from a data scientist?

In a world where technology is advancing at an unforeseen rate, having access to evidence-based insights combined with cutting-edge solutions is something that’s beneficial to organisations of all different sizes and specialties. 

Data scientists are particularly useful for organisations with marketing, technology and ecommerce platforms that attract a high number of users – seemingly small improvements on these platforms can result in huge increases in revenue.

There’s also a high demand for data scientists in the corporate sector. According to IBM, 59% of all data science and analytics jobs are in finance and insurance, professional services, and IT.

But, generally speaking, if your business is undergoing a period of major growth and transformation, and you’re facing intricate problems without any obvious solutions in sight, a data scientist can come in and identify the scale of your operations, hone your product and, ultimately, increase your organisation’s revenue.