Five top tips for when you can’t afford to hire a Data Scientist

Within the Data Science space, we speak with so many organisations daily, who are increasingly experiencing issues when it comes down to recruiting Data Scientists.

The first issue is that the job title ‘Data Scientist’ has been a catch-all job title. Some organisations are unclear as to what a Data Scientist actually does and what they expect them to do versus what they are realistically capable of doing.

The second and arguably the biggest issue is around salary expectations. Many organisations feel that Data Scientist salaries are unrealistic and unaffordable. So, when they are going to market, they are struggling to find the right people.

Where has the demand for experienced Data Scientists come from?

Data Science has been praised as the answer to aid recovery from the COVID-19 pandemic, so much so that many organisations are planning to drastically increase their data science headcount this year by a whopping 67%! Data Scientist roles are in demand and have been the number one job that companies are recruiting for since 2016. It dropped down to number three in 2020 and I imagine that this year (2021), it will be more in demand than ever before.

I’m sure that we all understand that increased demand for these types of professionals, coupled with not enough people in the market to fulfil the role, drives salaries up. Of course, Data Scientists are reaping the benefits of being in demand (and why shouldn’t they?).

As with all trends, they don’t last forever. But this is a trend that is predicted to last a long time: by 2030 it is predicted that there will be a global tech shortage of 85 million!

So, what do you do until then? What do you do when you don’t have the budget or the resources to recruit or you are simply unprepared to pay those kinds of salaries? I personally think that employers should try to be more realistic concerning what is possible and what is not.

There are, however, practical alternative options if you are willing to readjust your expectations, which I have listed below:

One: define the key responsibilities of the role.

Sometimes the organisations that we speak to are not even looking for a Data Scientist. The term Data Scientist can be quite an ambiguous term – so it is always worth checking that the naming conventions align with the role. I would always recommend defining the responsibilities of the role, rather than jumping straight into the fashionable Data Scientist job title. Because technology is much more accessible to non-data scientists these days, you may not need to hire someone who is a Machine Learning and AI purist. The actual role you may be looking for is more of centre of excellence position.

Two: attract the right talent!

Recruit people with the right attitude that have a hunger to learn, rather than experience to avoid those hefty wage bills. Firstly, decide what type of person you want to hire, rather than the job title. Prepare to go into the recruitment processes with an open mind and realistic expectations. Some of the organisations that we work with collaborate with universities to work with students leaving with a computer science degree or other analytical backgrounds such as statistics and engineering. There are plenty of great candidates out there who could prove to be every bit as successful, albeit with a bit of investment and patience.

Three: invest your time and your patience.

There are so many courses that employees with skill shortages can take. If you hire the right person and they are hungry and willing to learn, these people will go on to be excellent ‘Data Scientists,’ in the long term. It is a bit like looking for the next superstar in a sport, whether that be the Premier League or Rugby Football League. The reality is, if you want to pay the money for the finished article, it is going to cost, but if you are prepared to work with someone who has a great attitude and is willing to learn, you can unearth some great people.

Four: choose more agnostic technologies.

As technology develops and grows, it is becoming so much more user friendly. The likes of Microsoft are taking data science and simplifying it. In Microsoft Power BI, you are now able to build in predictive functionalities and Azure space – AutoML has drag and drop capabilities. This means you can effectively de-skill the workforce, which will save your salary bill.

Do not be constrained by particular types of tech – skills can be transferable. Especially moving from on-prem to the Cloud. Usually, employees with core skills in Python and SQL have good transferable skill too.

Five: call upon the experts.

At Simpson Associates we offer Data Science as a service for organisations that don’t have the expertise in-house, enabling you to boost your data science and business intelligence without the commitment of hiring. Plus, for your skill shortages, speak to Simpson Associates about our skills transfer service too.

In summary, do I agree with the salaries that top Data Scientists are commanding? Are they fair? The answer is that it is irrelevant what I or anyone else thinks – they are what they are. If you do not want to pay out for the hefty wage bill, then feel free to take on some of my tips above. Plus, you could be doing your bit to help to reduce the 85 million Global Tech shortage.

Questions

If you have any questions about data science or any of the tools mentioned in this article, please feel free to speak to us using our live chat, where one of our experts will be happy to speak with you in more detail. 

Blog Author

Andrew Edge, Data and AI Solution Lead, Simpson Associates