What was considered the most trending but uncommon job of the 21st century ten years ago is now facing open competition by data scientists. Indeed, data science has evolved dramatically in the last ten years. We have seen data scientists reskilling and upskilling themselves to cater to the dynamics of the digital market.
The evolution in data science has been a continuous process and so have been the skills of a data scientist. In the face of the current pandemic, data science has been the most sought-after job by young professionals as well as companies. Very young professionals are getting attracted to data science due to its lucrative nature. The companies are desperate for data science given its analytical and forecasting capabilities.
Few years down the line, it was very easy to land an entry-level research job with skills and knowledge of python data science, and machine learning. Over a period of time, the field became so saturated that junior data scientist jobs have become extremely competitive in nature. In this article, we examine the various factors that have led to saturation in the data science market.
The competitive edge
Aspiring data scientists are not limited to the fields of computer science and mathematics. Different types of academicians who have completed studies in the fields of statistics medicine and even psychology are looking for a career in data science. This is leading to saturation in the field at the level of talent acquisition. It needs to be noted at this point in time that companies have reported the screening of hundreds of resumes for data science jobs in the first stage itself. This puts companies in a very tough position because the screening of hundreds of applications for a single post is a time-consuming and cumbersome process.
The inflation factor
There is stiff competition for junior-level data scientists in the market. This is creating a lot of difficulties for making a successful career in data science. This imbalance in the demand-supply cycle of the job market is favoring senior-level data scientists who are getting highly paid by the companies. On the other hand, entry-level data scientists are fairly paid as per market standards but are paid less in comparison to senior data scientists. This makes sense because senior-level data scientists have knowledge of diverse fields and technologies including software engineering and DevOps. They are also experienced in big data technologies and cloud computing. The huge gap in the salaries of junior data scientists and the senior ones is also due to the expenditure and investment that the companies have to make on the training of junior data scientists initially. In most cases, the companies deduct the sum of training costs from the salaries of data scientists themselves.
The present pandemic
The covid-19 pandemic has taken the economy to its lowest ebb. The sectoral influences by the covid-19 pandemic have led to an economic slowdown and most of the erstwhile thriving companies are fighting for their survival. This has also prompted the companies to cut down their operations and lay off employees. Needless to mention, there has been a slow down in various sectors that are driven by data science. The influence has been relatively low as compared to other sectors. However, the growth rate of the data science industry has been drastically affected. In addition to this, one of the hidden consequences of the covid-19 pandemic has been the reluctance of the companies to hire data scientists.
We have witnessed intersectoral competition in the fields of data science, data engineering, machine learning, and cloud computing. While data engineering has witnessed massive growth in the last few years, data science has not been able to catch up with the rising curve. This is because data engineering and cloud computing have together enlarged the scope of their applications. As such, we have seen large-scale recruitment of DevOps specialists and data engineers. Machine learning engineers are also being constantly hired. Some of the jobs that require the skills of a data scientist are being taken by data engineers and DevOps specialists. However, this trend is seen mainly in the sectors of the cloud industry.
In spite of the major limitations listed above, the future of data scientists is quite prosperous. This is because data scientists are beginning to learn the skills of cloud operations and even DevOps. As such, the outlook of industries towards data scientists is witnessing a dynamic shift.