May Be You Are Already a Data Scientist

This post is not about how you can become a data scientist or to answer what skills, tools, and technologies you should learn to become an expert in this field. I’m writing this post for people who end up doubting themselves as a Data Scientist just because they are not using the latest tool set, platform, or simply just because they don’t have the title ‘Data Scientist’.

Every month, I meet for a coffee chat with a lot of Data Science enthusiasts from the industry, seeking advice and suggestions on how they can become a ‘Data Scientist’. Before I even begin to provide any advice, I usually ask them about their current and past work experience. To my astonishment, almost everyone was in one or more of the following categories:

• had a strong expertise and over 3 years of experience in writing complex SQL queries and creating traditional data models
• understands statistical methods like regression and can implement it in excel
• have been using traditional analytical tools like SAS SPSS for number of years

All the skills listed above are a subset of skills that a Data Scientist would normally possess, i.e. knows how to query data, understands statistical modelling methods, knows how to use a high level language to perform analysis. Although the above list of skills is not an exhaustive one, we should not forget that there is no such thing as a unicorn Data Scientist. Almost 60% of the people I met had at least one or more of these skills. So why were they all looking to become a Data Scientist when they already are one in some sense?

1. Most of them are simply chasing the title.
   Ever since the title ‘Data Scientist’ got glamorized as the sexiest job of the 21st century by D.J Patil in 2012, everyone wanted to have that title. Companies didn’t stay behind and they leveraged this title to lure in talent by renaming their traditional jobs that had anything to do with data as ‘Data Scientist’. I often encourage the aspirants to look beyond the title and dig deeper into the roles and responsibilities of the job. Sometimes, a true Data Science position may be disguised in the lamest job title.
2. Rise of the new Data Science frameworks
   People often get intimidated with all the new frameworks that have popped in the market in the past few years. For example, Apache Spark, Tensorflow, PyTorch, etc., and not to forget the endless platforms out there: AWS, Azure, Google Cloud, to name a few. Just because you don’t know any of them but know how to create a propensity model using SAS doesn’t mean you are not a Data Scientist. All these tools and platforms are just a medium with which Data Scientists implement their solutions. It’s alright to not know them as long as you know the underlying concepts.
3. Pay Gap
   Believe it or not, people doing vanilla analytics using latest tool sets are bringing a higher pay cheque home than people building statistical models using traditional tools like SAS who don’t have the title as Data Scientist, and that’s one of the reasons people are chasing the Data Scientist title as well.

So, what to do?

1. Sell the problem solving skills in the market
   You should market your abilities of being able to query data (agnostic of the underlying system being RDBMS or Hadoop), understanding of statistical methods of modelling, or any other transferable skills you have from your existing and past jobs, like story-telling. Given the pace with which the Data Science ecosystem is changing, employers are after people who can adapt to any tool in the future, so being platform and tool agnostic is useful.
2. Negotiate with the current or future employer
   Just because your current title or the title of your future job isn’t Data Scientist but you do most things Data Science doesn’t mean you should get paid less than the industry average. Learn the art of negotiation and reason with your current or future employer over the impact of the tasks you are performing. A good way of doing that is highlighting what the industry is paying for people who are performing similar tasks as you regardless of the tool set being used.
3. And the most important one… don’t do something because everyone is doing it
   Lots of people are willing to give up their years of IT experience in other fields like Computer Networks, ERP Systems, CRM etc. just to break into the field of Data Science. Why? a) Because apparently tech industry makes it sound cool to be a Data Scientist, b) assumption that Data Scientists are getting paid a lot, c) Everyone is doing it!
   If you think that a Data Science Manager is getting paid more than a Salesforce CRM Architect then let me burst your bubble.

So, before you begin underestimating yourself, your skill set, and try to become a Data Scientist, analyze your strengths and the work you are already doing. Who knows, may be you are already a Data Scientist.

Learn to create value using whatever work you are doing. There will always be new waves of technologies and job titles. What Data Scientists are today, Blockchain developers will be tomorrow and so on. If you are already good at something then don’t give it up just to follow what everyone is following. Regardless of whatever field you are in, if you can create value in your existing or future job then keep doing it. Become the master of your own domain.