It may be the “sexiest job of the 21st century”, but beyond that there isn’t a great deal of consensus on how to define a data scientist.
Part of the reason it’s so hard to pin down a meaningful definition of a data scientist, is because the scope of what a data scientist does seems impossibly broad. The organizations that make some headway on developing their data science capabilities are the ones that emphasize the cultivation of data science teams – which come together with a variety of backgrounds and technical capabilities – instead of trying to identify and recruit the proverbial “unicorn,” single rock star all-round data scientist. But even so, how does one begin to approach the task of putting together a data science team without simply resorting to a simplistic approach of peppering job descriptions with “Hadoop”, “machine learning”, and other buzzwords de jour?
These have become pressing questions in the big data scene in Malaysia. In May 2012, Big Data Malaysia, a professional networking group, was set up to help connect the ‘supply and demand’ of big data talent. Amongst our network we now see many startup, corporate and government initiatives being pursued. In mid-2013 we organized a broad survey of our community to get a better sense of interests, activities, and challenges faced by the group. Amongst other things, we looked into the issue of skills that were in demand.
