Data science is a relatively new field that is constantly evolving and rather difficult to define at times. At present, no company can afford to ignore the value of data in growing their business. For this reason, a growing number of companies are employing data scientists as individuals and in teams in order to make sense of the growing goldmine that is consumer and market data.
In this article, we will explore the future demand of data scientists. Since the importance of data science in business was covered in another article, the focus here will be on the likely
change in the nature of a data scientist’s day-to-day function.
Future Demand for Data Scientists
1. Job Losses Due to Automation
Data scientists automate processes in order to streamline and simplify business and get the latest, most accurate results and insights in an instant. This has led many to believe that this field could be obsolete in the space of ten years, since data scientists would, in effect, automate themselves out of a job.
These assumptions are based on flawed data. In order for data management systems to work properly and effectively, human oversight is still necessary, even if only at a high level. Machines are able to take care of highly complicated and sophisticated calculations, but they will never be able to replicate the intellectual thinking of humans. For this reason, the daily work of data scientists is likely to become increasingly intellectual and higher-level with an enormous collaboration and interfacing with complex data science algorithms. According to indeed.com, there are currently 11 000 open positions for data scientists, which
is living proof that data scientists are, in fact, not working themselves out of a job and that industry and business are both in dire need of skilled, trained data scientists.
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2. Clearly Defined Career Paths:
Data science is an interesting amalgamation of various disciplines and fields of knowledge. For this reason, few people employed as data scientists are pure data scientists, in the manner that you would find pure statisticians or engineers. As this field progresses, the definition of a data scientist and their role within a company is likely to become more clearly defined. This would allow young professionals entering the workforce to choose a clear career path as a data scientist as opposed to the present, almost hodgepodge approach to building a career in data science. Study opportunities focusing purely on data science are
also likely to emerge.
At present, data scientists are able to add immense value to a company at a relatively early stage in their career. This is in part due to the nature of the field, since these experts explore areas that are central to fast-tracking business growth. A drawback of this is that staff members could feel that they have reached their peak at an alarmingly early stage of their career, potentially leaving them despondent and feeling that there is no further possibility for career growth.
As this field grows and matures, this hurdle to professional growth is likely to be overcome, leading to clearly defined career paths and excellent opportunities for professional growth. Even as we speak, there are some senior-level employees who function purely as data scientists, effectively acting as a role models and mentors to younger data scientists. This indicates the ability to have a senior-level impact on a company as a data scientist.
3. Function in the Workforce:
Data scientists answer business and industry questions based on solid research. To this end, they leverage large volumes of data originating from several internal and external sources.
In order to employ predictive and prescriptive modeling, data scientists employ sophisticated analytics programs, machine learning, and statistical methods. After employing these models, the data is explored and examined in order to find hidden patterns that could be useful to business or industry. In the future, a portion of this function could be automated, but there are real limits to automation, as will be discussed in a later section.
Armed with these insights, data scientists will lead executives and key stakeholders to make informed decisions for the future of their businesses. It is clear that data scientists would function as trusted and skilled advisors to executives and stakeholders.
4. Widespread Automation and Machine Learning:
Machine learning, or ML, algorithms automate and simplify complex computational tasks, leaving personnel free to pursue complex intellectual problem-solving. This is good news for data scientists, as their daily function will likely become more complex and challenging, leading to greater job satisfaction.
Smaller companies often cannot afford to employ an entire team of data scientists. Since this field is incredibly broad, it is uncommon to find one person possessing all the skills required for effective data science application in such a company. Here, sophisticated automated systems would allow a skilled information analyst to partially take on the tasks usually reserved for a team of data scientists. As a result, pure data scientists, much like statisticians and actuaries today, are likely to only be employed as a team by large corporations since these corporations would have the funds available to finance an entire data science team.
In order to adequately develop data scientists at a tertiary education level, academia must re-evaluate and update programs the and train these future professionals at a pace that keeps up with the breakneck speed of current technological advancements and changes in industry and business.
5. Citizen Data Scientists:
A knock-on effect of the sophisticated automation of data processing and machine learning tasks is that staff members with little or no training in data science are now able to try their hands in the data science field at a lower level. This will lower costs for smaller businesses and broaden the reach of machine learning and artificial intelligence vendors.
As an added bonus, the skills gap between highly qualified data scientists and the average staff member will be overcome, increasing the efficiency of data processing and related decision making. Here, a word of caution is necessary: if the robustness of data governance and analytics is not carefully governed and monitored, the laymen taking on data science tasks in conjunction with process automation could inflict irreversible damage on a business or system. At some point, a trained data science expert would be needed in order to verify processes and results.
With this result in mind, future data scientists may well become trainers, educating non-data science staff to use the “self-service” models effectively through formal “onboarding” programs.
Advanced data visualization will remain the domain of highly skilled data scientists.
6. Limits to Automation:
While machine learning algorithms are capable of an impressive array of tasks, there are some tasks that cannot be automated. Prime examples are data wrangling and data visualization. In data wrangling, raw data is converted into a format that is machine-readable. To this end, keen human judgment is needed, which machines are simply not capable of. Data visualization involves the personal interpretation of data. A data scientist has to interpret results spewed out by machine learning algorithms in order to arrive at logical, actionable and business-savvy decisions. These decisions must be communicated to C-suite executives in order to enable them to develop sound business strategies.
7. More Complex Projects:
Since automation would have such a large effect on the day-job of a data scientist, their job is likely to become much more complex and collaborative as larger projects are undertaken. The effect of these projects is likely to entail a much greater reach in the relevant industry. In order to be cost-effective, open-source tools are likely to be employed by data science teams. This is also a knock-on effect of data science becoming more widespread and easily accessible to the public.
8. In-Demand Skills:
According to towardsdatascience.com, the skills that are most in demand for data scientists at present are, in decreasing order, analysis, machine learning, statistics, computer science, communication, and mathematics. There are a host of other skills required in order to fill the position of data scientist, but these are the most prevalent ones. From this, it is clear that data science is a highly technical, complex undertaking and it is likely to remain such for a very long time to come.
From the above article, it is clear that data scientists will be in high demand in the near future and that their roles will become more clearly defined and complex as time progresses. It is important for higher learning institutions to keep up with changing technology and the ever-evolving needs of industry and business at large. To this end, training programs should be updated to prepare and equip graduates and young professionals to enter the workforce with a relevant and useful skillset that will add value to any company.
Senior Data Scientist and Alumnus of IIM- C (Indian Institute of Management – Kolkata) with over 25 years of professional experience. Specialized in Data Science, Artificial Intelligence, and Machine Learning.