The issue with in-house data science teams
Let’s pretend for a moment that the skills shortage in the data science job market does not exist. Even then, the costs remain a burning issue when building in-house data science teams. In the US, the estimated total pay for a data scientist is $120,508 per year. Varied reports reveal that data scientists in Germany earn between €55,700 and €63,000 per year. In the UK, their salaries start at €60,000. And this is just a fraction of the total cost of sustaining a single position in-house for a company.
Apart from the lack of available skills and high pay expectations, another argument against hiring in-house data scientists is the dynamic nature of data-driven projects. Demands are constantly shifting. For example, now, you may want to build a powerful data engine that links fuel use with mobility patterns of individual drivers or optimizes routes to preserve the residual value of your fleets. But once that’s done, your company may not have the data needs sufficient to sustain a team of highly-paid specialists full time.
For these reasons and more (which we are happy to discuss with you on a quick consultation call), data science outsourcing is a popular option among future-driven companies that want to protect their growth. In this arrangement, an organization delegates all tasks related to data science to an external, specialized company that works exclusively on data science projects. These tasks include:
- and visualization of data.
Hiring specialized support to fulfill one’s data science needs means you retain your intellectual property rights and ownership over the data and the insights, but hand off all work requiring technical data science expertise to an external team.