In the world of data, the roles and responsibilities can be quite ambiguous and complex. With various perspectives to consider, it can be challenging to understand the distinctions between different job titles under the data umbrella. When you are a technical expert, this is much easier to do but when you are from the commercial side this information is not easily accessible or readily on your radar.

This article aims help those of us who are not from a technical background and explain individual nuances of the data revolution. I will discuss the roles, similarities and differences between data scientists and research scientists. By the end of this article, you should have a better grasp of these positions and how they contribute to the larger data ecosystem.

A data scientist is an analytics professional who is responsible for collecting, analyzing and interpreting data to help drive decision-making in an organization. The data scientist role combines elements of several traditional and technical jobs, including mathematician, scientist, statistician and computer programmer. It involves the use of advanced analytics techniques, such as machine learning, deep learning, natural language processing and predictive modelling. As part of data science initiatives, data scientists often must work with large amounts of data to develop and test hypotheses, make inferences and analyze things such as customer and market trends, financial risks, cybersecurity threats, stock trades, equipment maintenance needs and medical conditions.

What is a Research Scientist?

A research scientist is a professional who gathers information by performing both theoretical and experimental research in a specific scientific field. A research scientist can work in many different areas, such as medicine, natural science, computer science, and social science. Research scientists collect data, interpret the results of experiments, and form hypotheses in an attempt to answer questions about the natural world or the man-made world, depending on their field. They can be found working in government agencies, as well as private companies.

Similarities:

Data and research scientists have a wide range of career options available to them, spanning various industries such as technology, engineering, medicine, banking, and business. Typically, data scientists tend to work in the private sector, whether it’s for companies, private research facilities, or on a freelance basis. However, it’s not uncommon for data scientists to work for government agencies as well. Research scientists, on the other hand, can work in both the commercial sector and as government consultants, but a large proportion of them are employed by academic institutions. Research scientists who work in universities and colleges have additional responsibilities compared to their counterparts in industry. They have to balance their research activities with teaching and mentoring students.

Differences:

To become a Data Scientist, you don’t necessarily need an advanced degree, but most job postings prefer applicants with a master’s degree. On the other hand, Research Scientists typically hold a doctorate in their field of study. Although some may have a master’s degree, most require a doctorate to gain the expertise needed for their work. Research Scientists focus on a specific domain of knowledge, conducting extensive research on complex issues. Many of them also have additional responsibilities as educators in higher education. Pursuing a doctorate in their area of expertise equips them with the knowledge and skills to formulate significant research inquiries and design studies and experiments to address those questions.

These professions also differ in their level of practical application. While data scientists work on projects with immediate applications, research scientists often study theoretical concepts that may not have a direct, practical impact on the real world. For example, an astrophysics research scientist strives to understand how physics relates to the interpretation of astronomical observations. In this process, a data scientist’s role might be to develop a computer program that assists the research scientist in testing models and analyzing data.

Conclusion:

Both Data Scientists and Research Scientists share similar responsibilities. They both analyze information to identify trends and develop theories based on their findings. However, their work is focused on different goals. Data Scientists typically work for organizations and use their findings to improve the organization’s bottom line. Research Scientists, on the other hand, work on theories and their research either supports or refutes these theories, which can lead to the development of new theories.

When considering a career in science, it’s important to think about your interests and professional goals. Data and Research Scientists can specialize in different areas, so you should choose a path that aligns with your interests. If you enjoy working with complex computer systems and designing processes that collect, analyze, and model data, then a career as a Data Scientist might be right for you. Alternatively, if you enjoy studying theoretical concepts and aspire to work in higher education, a career as a Research Scientist could be a good fit.

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