Today, the world of data is changing rapidly and two professions data scientist and statisticians have emerged at the forefront of this transformation. There is a large gap in the earnings of these two professions, even though they deal with data. Historically, the data scientist engineer salary has been more than that of the statisticians.
This article explores the possible reasons for this disparity in the salaries of data scientists and statisticians.
1. Scope of Work and Responsibilities
One of the main reasons why there is such a big gap in the earnings of data scientists and statisticians is the scope of their work and the responsibilities they carry out. Traditionally, statisticians have carried out tasks such as data collection, analysis, and interpretation to draw inferences and present them to the relevant stakeholders. They reach conclusions by using different established statistics models, methods, and techniques. The work they do is very important, but their work is limited by scope in sectors like health, government, or academics.
In contrast, data scientists, carry out several responsibilities in addition to performing many tasks usually carried out by statisticians. They perform statistical analysis and use data engineering, machine learning, and data visualization concepts to analyze and interpret the data. They deal with large & unstructured datasets to uncover hidden patterns and build predictive models that can be applied in domains like fintech, technology, e-commerce, and more.
Their work is multi-disciplinary as the data scientists have to use techniques and frameworks related to computer science, mathematics, and more. They are also required to possess domain expertise. This all justifies them being paid on the higher end as compared to statisticians.
2. Demand for Data Science Skills
Digitization of different industries, and the boom of big data are some of the key factors that explain the sudden increase in demand for data professionals like data scientists. In a way, this also explains why the data scientist engineer salary is more than other data professionals like statisticians.
Companies, across industries are looking to exploit the data boom and use the power of data for decision-making and automation.
While, on the other hand, statisticians are no more the hottest resource. Their demand, across the board, is declining steadily. Even though the traditional statistical methods for data analysis are critical, they are not sufficient in this age of big data. Instead, modern tools and techniques of data science, like machine learning and deep learning are better suited to meet today’s data challenges.
Further, many statistical jobs do not require one to possess complex technical knowledge about coding, database management, etc. which are intrinsic in data science. Therefore, a combination of skills mastered by data scientists with knowledge of popular programming languages like Python, R, and even expertise in AI technologies gives them an edge over statisticians in terms of earning potential.
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3. Tools and Technology
While statisticians generally focus on computer software packages like SPSS, SAS, and R for statistical work, data scientists typically apply these and many other tools-including Hadoop, TensorFlow, and Spark-for doing their work. All of these technologies help the data scientist to deal with big data and apply machine learning to produce or develop business insights and automatically generate models for processes.
One reason why the data scientist engineer salary is substantially higher is that the tools they use are much more complex and large in scale. Data scientists are expected to take care of the databases, clean up, and pre-process data, build models to deliver real-time predictions.
In contrast, the tools used by statisticians are not so designed by popular necessity for dealing with the enormous varieties of data that come along with data scientists.
Another reason is that industries are looking at experts who can use such cutting-edge technologies as cloud computing and artificial intelligence; certainly, these are strengths where data scientists are successful. Deep technological expertise alone justifies higher compensation for data scientists versus statisticians.
4. Cross-Functional Collaboration
Another reason for higher data scientist engineer salary is simply because of their role in cross-functional teams. They often collaborate with departments as marketing, operations, product development, IT, and more to inform business decisions and improve processes. Organizations value the ability to communicate complex findings effectively to non-technical stakeholders.
The antithesis of this, statisticians tend to be very siloed in roles, usually research or policy driven groups. Their work is important, but it doesn’t have the same sort of actionable business linkage that a data scientist’s work does. The influence on business strategy and innovation means that companies can pay data scientists at a higher rate.
5. Higher Expectations and Broader Career Pathways
Data scientists need to not only analyze but also get actionable insights that can be used for innovation and revenue generation. More importantly, they are required to build models that can predict trends, optimize processes, and automate decisions across functions in a company. The sheer kind of impact this kind of work could have on a company’s bottom line translates into a higher salary package.
Further, a career path for a data scientist is much broader and remunerative. Data scientists typically work up to machine learning engineer positions, AI specialists, or data science managers, and these positions pay much more. The scope of career advancement in this field is much more restricted for statisticians, most statistical scientists work up into academia or research-oriented positions where the remuneration may not scale correspondingly.
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Conclusion
The difference between the data scientist engineer salary and that of a statistician comes from the complexity, scope, and effect of their work. Data scientists bring a more diverse and technologically advanced set of skills to the table, and they contribute directly to the growth and innovation of companies in most industries.
As businesses are making more and more data-driven decisions, the data scientists will be in even more demand, which will further increase the gap in their salaries as compared to statisticians.