There are many axes along which we can classify data scientists.
We can classify based on the primary specialty, in terms “analytics”, “business intelligence” and “machine learning”. We can classify based on domain, into “financial data scientists” and “retail data scientists” and “industrial data scientists”. We can classify by the choice of primary software tool, into “R data scientists” and “Python data scientists” and “SAS data scientists”. We can also classify by expertise, such as “deep learning” and “statistics” and “stochastic calculus”. The axes are endless.
Here is my not-so-humble attempt to contribute yet another such axis based on my observations in the industry – “technology facing” and “business facing” data scientists.
Technology facing data scientists put the software first. You’ll see them building pipelines, making sure their solutions can be easily integrated into the software stack, and worrying about how quickly their analysis can run. They will spend a lot of time on data engineering and infrastructure works, and their first concern when designing a solution is that it should be easy to implement. They are highly process oriented and not so fond of hacks.
Business facing data scientists, on the other hand, are primarily concerned with insights, and don’t care much about technological niceties. The technological feasibility and ease of implementation of a solution is an afterthought. Their data is messy, and the process is not easily repeatable (might even involve some manual processes). But they make sure that the insights they draw can be easily understood by a human, and invest time and effort in communication and visualisation. They might even build tools to help the business side of the organisation understand what is happening in the model.
This distinction is actually unsurprising if you look at who the primary clients of these respective types are. The business facing data scientists are more likely to be employed in generating insights, and building models to try and understand what is happening. The technology-facing data scientists will have spent most of their careers building production systems, and are thus very well acquainted with the software engineering process.
It is important, however, to recognise this distinction, and employ the data scientists as per their specialisation. A technology-facing data scientist in a business-facing role might be seen as spending way too much effort in getting the technology right, and doing her own thing while being unmindful of the business clients. A business-facing data scientist in a technology facing role will end up producing messy solutions that may be insightful, but will be a nightmare to implement.
This was first posted on LinkedIn