5 min
Amid buoyant reports of soaring demand for data scientists – expected to touch 700,000 openings by 2020, there is an alarming sentiment that underpins the positive outlook. Is the market being flooded with too many data scientists and given the rush to upskill, how will the jobs market grapple with the ensuing flood of Masters postgraduates who finish data science programmes? Most industry experts believe there may be too many data scientists in the field now, but few are actually any good?
Over the years, data scientist has become an umbrella terms – it’s come to define a professional who is part-statistician, part CS expert with a mix of generalized and specialized analytical skills such as R, SQL, Apache Hadoop, MapReduce, Pig and data mining skills among others. Another critical skill is domain knowledge about the sector one wants to apply the data in.
However, if we talk about the demand for data scientists specifically, we believe that the job market is still soaring for skilled data scientists and it isn't on a decline. In fact, McKinsey famously predicted in 2011 that by 2018 there would be 2.8 million workers with either deep analytical talent or data-savvy skillsets. And by 2015, the United States job market reported over 2,350,000 job listings for core Data Science and Analytics (DSA) job functions. A Quant report by IBM and Burning Glass indicates that closing the Data Science talent gap will require new strategies for up-skilling and re-skilling the incumbent workforce. And fresh graduates alone will not close this gap. Even on the salary front, DSA jobs command good salaries with entry level graduates netting an average of $80,265.
There may come a time when the hype dies down a bit and the salaries will scale down. Also, with the influx of tools, shifting technologies and data literacy on the rise, people will become more data savvy and a time may come when the job of a data scientist will be finally demystified.
So, is there a gold rush type mentality to data science? Will this trend also meet the dotcom boom? AIM weighs in:
Is there an excess of newly-minted data scientists: To start with, it is always difficult to find the right data scientist with a magical combined skill set of statistics, programming, project management skills and business knowledge. The so-called Data Science Bootcamps or MOOCs Bootcamps do not dive deep into the full length and breadth of data science, online lessons are not always practical, and projects/assignments cannot replace the true skills or experience one can gain from a real-world work experience. Recruiters reveal the majority of people interviewed can be clubbed under the “Data Science Analysts” bucket. Online classes and swift PG programs are churning out so-called entry level data scientist. Are businesses relaxing the JD of data scientist according to their needs: Besides a gamut of high-demand skills, organizations look for someone who has the technical expertise, the required skillset and can deliver a quick turn-around. As the profile of Data Scientist is relatively new, many enterprises have their own way of defining the role that comes with a different set of expectations. Since there is no one-size-fits all solution for your business problem, it is not uncommon to find organizations to tweak the Job Descriptions according to the business needs. One data scientist shared his experience on a forum, revealing the day-to-day functions included running A/B tests, developing spreadsheets and using Tableau for visualizations. Also, he added that most top organizations are grappling with one major problem -- where they need analytics but don't need “too much” analytics.
What is the career path for a Data Scientist? So, you landed a great job as a Data Scientist and have been hard at work to help the company grow. Ever wondered about your promotion, whether it is going to be based on your ability to handle a team, improve business outcomes or manage that analytics project independently or develop a great product experience? is there a defined framework for advanced analytics roles concerning promotions in the analytics ecosystem? The range of analytic skills required in this role are so diverse and the job requirements vary from one organization to another (for eg: hiring and training resources or strengthening product). Many analytics professionals revealed in forums that there are no defined ways to measure the output of insights produced or measure the real business value a data scientist brings to the job. In most cases, if the business objectives aren’t met, then measuring the ROI of your work becomes difficult.
Can MS programs and MOOCs churn out high-quality professionals: Think again, not a single day passes by when we don’t hear about a new Boot Camp, training video, MOOC, Data Science Meetups, blogs, conferences and different Heads of Data Science actively promoting the up and coming field. Here’s the harsh truth – data science enthusiasts who are investing their time in MOOCs have a lot of ground to cover if they don’t hail from a Math or statistics background. And secondly, most importantly, not everyone being trained in statistical modelling or predictive analytics will go on to become a data scientist. Of late, there is a growing need for professionals in marketing and operations roles who can juggle both the business requirements and understand how analytics works. Case in point, a management consultant with data science training will have a definitive edge over the competition.
Are the right people training to become data scientists: Data science is a lifelong commitment and the learning never stops. It is great field for those who have an analytical bent of mind, enjoy challenging themselves intellectually and love to develop expertise even on the job. But, it’s a terrible career choice for anyone who doesn’t have a sound technical background. Of late, we see a lot of people from Digital Marketing and HR functions switching gears mid-career but do they have the requisite background to successfully transition to Data Science and add value to their workstream. So, are the right people training to be data scientists?
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