Data scientists are not dime a dozen and they are not in abundance as well. Buzz around bigdata has produced a job category that is not only confusing but has been costing companies a lot in their stride to look through the talent pool to dig for a so called data scientist. So, what exactly is the problem and why are we suddenly seeing a lot of data scientist emerging from nowhere with very different skill sets? To understand this we need to understand the bigdata phenomena.
With emergence of big data user companies like Google, Facebook, yahoo etc. and their amazing contribution to open source, new platforms have been developed to process too much data using commodity hardware in fast and yet, cost efficient ways. Now with that phenomenon, every company wants to get savvier when it comes to managing data to gain insights and ultimately building competitive edge over their competitors. But companies are used to understanding small pieces of data using their business analysts. But talk about more data and more tools. Who will fit in? So, they started on lookout for special breed of professional that have the capability to deal with big data and it’s hidden insights.
So, where is the problem here? The problem lies in the fact that only one job title emerged from this phenomenon- data scientist. The professionals who are currently practicing some data science via business analysis, data warehousing or data designing jumped on the bandwagon grabbing the title of the data scientist. What is interesting here is that data scientist job as explained above does not deserve a single job description so it should be handled accordingly. It was never a magical job title that has all the answers for any data curious organization, to be able to understand, develop and manage a data project.
Before we go into what companies should do, let’s reiterate what is a data scientist. As the name suggest, it is something to do with data and scientist. Which means it should include job description that has done some data engineering, data automation, and scientific computing with a hint of business capabilities. If we extrapolate, we are looking at a professional with computer science degree, doctorate in statistical computing and MBA in business. What would be luck in finding that candidate and by-the-way, they should have some industry domain expertise as well. What is the likelihood that such a talent exists? Rare. But, even if they are in abundance, companies should tackle this problem at much granular and sustainable scale. And one more thing to note here is that no two data scientist job requirements are the same. This means that your data scientist requirement could be extremely different from what anyone else is looking for in a data scientist. So, why should we have one title to cater to such a diverse category?
So, what should companies do? First it is important to understand that companies are building data scientistsâ capabilities and should not be hiring the herd of data scientists. This means that companies/ hiring managers should understand that they are not looking for a particular individual but a team as a solution. It is important for businesses to clearly articulate those magic skillsets that their so-called data scientist should carry. Following this drill, companies should split the skillset into categories, Data analytics, Business analyst, data warehousing professionals, software developer, and data engineers to name a few. Finding a common island where business analysts, statistical computing modelers and data engineers work in harmony to address a system that handles big data is a great start. Think of it as putting together a central data office. Huh! another buzz word. Donât worry; I will go into more details in the follow-up blogs. Think of it as a department where business, engineering and statistician work together on a common objective. Data science is nothing but an art to find value in lots of data. So, big-data is to build capability to parse/analyze lots of data. So, business should work through their laundry list of skillset. First identify internal resources that could accommodate that list. Following this, companies should form a hard matrix structure to prove the idea of set of people working together as a data scientist. BTW I am not saying that you need one individual from each category, but, together the team should have all the skills mentioned above
One important take away for companies is to understand that the moment they came across a so called data scientist, it is important to understand which side of data scientist the talent represents. Placing that talent in their respective silo will help provide a clearer vision when it comes to understanding the talent and understanding the void that could stay intact if the resources are not filled accordingly. So, living in this convoluted world of data scientist is hard and tricky. Having some chops into understanding data science as a talent, companies could really play the big data talent game to their advantage and lure some cutting edge people and grow sustainably.