The advantages of Data Science on Demand over Full Time Hiring

The importance of data science as a tool to enable businesses to be the disruptors and trend setters has rapidly gained prominence. The data collected from sales, operations and trends on social media are increasingly valued to develop business intelligence. This development has invariably been accompanied by an immense demand for data science professionals. This phenomenon has given rise to a new, increasingly important, role: the freelance data scientist.

Obtaining the services of a data scientist is not an easy task as a result of the demand for skilled data science talent eclipsing the supply. The gravity of this scenario is felt more so by SMEs, given their limited resources and budget. This being the case, SMEs thus have three options should they wish to adapt data science into their business model. These include (1) hiring a full‑time data scientist; (2) trying to perform data analytics themselves using special apps and data analytics tools; and (3) bringing in a freelance data scientist for specific projects. Each of these options has its own merits. However, the most suitable alternative for SMEs would be to hire a freelance data scientist, given the advantages discussed below.

The needs for a typical SME are naturally still evolving. An example is a new travel agency using weather data and flight data to prepare custom holiday packages. This is a seasonal function which can be standardised using algorithms. Typically, SMEs would have, if any, limited budgets for data analytics projects.

Furthermore, there is normally a significant time gap between projects requiring data analytics. Retaining permanent services of a data scientist during such a time gap seems not feasible. Instead, hiring a freelance data scientist for each project is reasonably more logical for SMEs. This alternative offers them the flexibility they need as an evolving small business. Specifically, the usual payment method for paying a freelancer is based on milestones delivered. This offers SMEs flexibility on their expenses, given that they would most likely be stretched for capital.

Furthermore, freelancers would not receive, nor would they expect, company benefits. Hiring a freelancer also offers SMEs flexibility in terms of time. Conventionally hiring a full‑time data scientist would include advertising for the post, conducting interviews and selection processes, and onboarding once selections are complete. The time saved when hiring a freelancer, which would take only a fraction of full‑time hiring, can be used by SMEs to focus on maintaining sustainable business growth.

Hiring a freelancer conceptually allows SMEs to essentially “retain” data science talent. Given the scarcity of skilled data science talent, data scientists would understandably always be on the lookout for new challenges and better pay scales. As such, keeping hold of a data scientist may not be easy even for big businesses, more so for SMEs. However, with access to freelance data science talent, SMEs have the capability to essentially “retain” the talent, although it may be different individuals.

The freelance model offers data scientist who seek novel problems to solve (in order to gain invaluable experience) the opportunity to do so. Such data scientists would thus appreciate the challenges faced by SMEs and would be keen to consult for SMEs on data projects. It is essentially advantageous for both parties, with SMEs probably appreciative of the capability to have access to data science talent since they would not have enough resources to retain the services of a full‑time data scientist.

The virtues of hiring freelance data science talent by SMEs are justifiable and not limited to the ones discussed above. Start‑ups and SMEs do not need to struggle to obtain data science talent. Hiring freelance talent offers them much needed flexibility and an ability to operate effectively within their means.

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