9 Types of Data Scientists | Which One Should You Become?
By Rohit Sharma
Updated on Mar 27, 2025 | 6 min read | 6.6k views
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Data science has become an essential part of modern business operations, leveraging techniques like data mining, machine learning, and visualization to transform vast amounts of structured and unstructured data into valuable insights. As companies increasingly rely on data-driven decision-making, the demand for skilled data scientists continues to grow across industries like Marketing, Sales, Engineering, and Product Development.
A career in data science offers immense opportunities for specialization and growth, blending expertise in Statistics, Mathematics, and Computer Science. Based on their roles and focus areas, professionals in this field can be categorized into different types of data scientists, each playing a crucial role in extracting, analyzing, and interpreting data for business success.
Product-focused Data Science
Researchers
A data researcher is good with crunching numbers. Usually, people with a background in Statistics go into this field. Statisticians and academicians possess some core skills like hypothesis testing, visualization, and quantitative research.
Among the various types of data scientists, data researchers play a crucial role in applying statistical models and theories to analyze key business issues, helping companies make informed decisions and improve performance.
Developers
Among the various types of data scientists, data developers focus on creating algorithms that drive product and pricing strategies. They analyze big data to identify patterns for demand forecasting, which is crucial for inventory and supply chain management. With strong programming and machine learning skills, data developers also collaborate with statisticians to solve complex business challenges, making them a vital part of the data science landscape.
Creatives
Data creatives handle huge mounds of data to come up with innovative tools. This group helps in the creation of a flexible and learning organization by developing new ways of thinking and learning. Using data as a compass, these people use their scientific skills and out-of-the-box ideas to transform processes and systems. Data science creatives stimulate the evolution and growth of an organization by continually improving and shaping the way things are implemented. Read: Top Data Science Tools
Business Practitioners
This group uses its business acumen and technical prowess for making vital business decisions. They are not only familiar with data science but are also equipped to derive valuable insights and apply the same to real-life situations. As a result, they create an overall culture based on the principles of logical thinking and evidence-based decisions.
Business Intelligence-based Data Science
Quantitative and Exploratory
Equipped with in-depth knowledge in data science, they combine academic theory with quantitative and exploratory research to improve technological and other products. These are the deep thinkers, hard workers, and persistent explorers who want to find out how things work by applying sampling theories, developing predictive models, and carrying out experimental design, among other things.
Product Scientists and Engineers
The data scientists also work the areas of product management and engineering. Here, their task is to design, build and manage the available information to understand how products are made. They further use analytics to fine-tune the products. The financial success of the organization depends on what it offers to the consumers. So, data scientists bring in their competencies to bolster the competitive advantages and help the business tide over contingencies
Operational Data Scientists
This group of data scientists is adept in data management and works closely with the different teams within modern organizations, such as finance, sales, and operations. Their task is to analyze trade processes, client reporting, performance, responses, behaviours, strategies, and efficiencies to support problem resolution. Operational data scientists work closely with client support teams, operations and functional managers to ensure the integrity of data systems. Adequate knowledge of Statistics and Operations Research is required for entry-level positions in this field.
Marketing Scientists
This type of data scientists is responsible for delivering customer value and driving organizational profitability and growth. Marketing data scientists are concerned with the user base of the product. They use their knowledge of data science to evaluate performance and improve efficiency, just like the regular marketing staff.
We are currently living in the digital era, where businesses generate and manage large quantities of data. The environmental complexities make it almost essential to use this vast information to evolve and survive in a competitive market continuously. After all, businesses do not function in a vacuum!
Research Scientists
Research data scientists are good with handling large data sets. Their work may not be directly tied to the organization’s outputs, but it is crucial for activities like report-making, summary presentations, and other analytical purposes. The skills of this type of data scientists are especially useful in large think tanks and financial and research institutions.
So, data science is a wide-ranging in essence and equally vast in its applications. The current rise of artificial intelligence has made it essential for businesses to employ different types of data scientists who can make sense of huge quantities of raw data generated in the digital world.
Surviving in the ever-changing, technology-led workplace is another challenge. As a result, companies are training their staff, and routine employees are looking to up-skill. Based on an organization’s requirements, employers are looking for new recruits and seasoned professionals to work at different levels and in diverse areas.
Data scientists are employed not just in the information technology sector but also in healthcare, banks and financial institutions, research organizations, as well as management and consulting firms. Moreover, everyone from engineering graduates to MBA-holders to doctoral scholars is intrigued about data science and wants to learn more about it.
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Conclusion
With a diverse range of applications, data science has become a highly sought-after field among employers, students, and professionals. As businesses increasingly rely on different types of data scientists to drive decision-making and innovation, the demand for skilled experts continues to grow. It is today’s buzzworthy career that is only going to pick up pace in the future!
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