9 Different Types of Data Scientists [Which one You Should Become?]

Data science has become an integral part of day-to-day business operations today. It incorporates techniques like data mining, cluster analysis, machine learning, visualization, etc. to turn large quantities of structured and unstructured data into a valuable resource for the organization. 

Data science has become one of the most sought-after disciplines of study in recent times, mainly because it is not a fad, but a field with immense career advancement and specialization opportunities. Modern-day companies employ data scientists to contribute to a wide range of operations, including Products, Marketing, Sales, and Engineering. The qualifications required for each role vary in terms of education, experience, skill level, and aptitude. 

Data science is a mix of Statistics, Mathematics, and Computer Science. Typically, as a data scientist, your work involves data collection, information processing, analysis, problem-solving and drawing actionable solutions. Data science can be categorized into two broad classes – Product-focused data science and business intelligence-based data science. Further, there are multiple sub-groups under each category. Let us delve into the different types of data scientists one by one. 

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. Statistical models and theories can lend a helping hand to corporate actions by diving deeper into key issues that influence performance.

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Developers

Data developers come up with algorithms that can guide product and pricing strategies. Moreover, they help extract patterns from big data inputs for demand forecasting, which plays a significant role in inventory and supply chain management. Since these people have strong programming and machine learning skills, they can also assist and work alongside statisticians to solve critical problems. 

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

So, with a diverse range of applications, data science has emerged as an in-demand field and an area of interest among employers, students, and professionals alike. It is today’s buzzworthy career that is only going to pick up pace in the future!  

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