For enquiries call:



Aage ki Socho

HomeBlogData Science15 Different Types of Data Scientists [with Responsibilities]

15 Different Types of Data Scientists [with Responsibilities]

13th Sep, 2023
view count loader
Read it in
12 Mins
In this article
    15 Different Types of Data Scientists [with Responsibilities]

    Data Science has gained a lot of popularity in the last few years. They have penetrated into almost all domains, from eCommerce to Health care. Even though being one of the most trendy topics, it is still ambiguous about what work people in this field do. One can classify the types of data science professionals depending on the outcomes they deliver. AI/ML Engineer, Data Analyst, Actuarial Scientist, Mathematician, Digital Analytics consultant, etc., are some of the roles which fall under data science. This article will provide a detailed description of different types of data scientists and their responsibilities.  But before beginning, it is preferable to have fundamental knowledge about data science; taking the best Data Science certification available in the market will help in forming a foundation for it.  

    Data Science is a highly promising and lucrative career. Hence, its popularity is increasing worldwide. As I already mentioned, there is a lot of confusion around what exactly data science is, and even a lot of experienced professionals have a scattered understanding of the topic.   

    What Does a Data Scientist Do?

    A Data Scientist is a person who uses his/her skillset to gather, manage and store data for analysis. This is not easy as it sounds; a single person cannot do handling and engineer data. Proficiency in programming, mathematics, statistics, databases, etc., are some of the basic skills required to pursue different data science types of activities. 

    Creating various types of data science models is the key responsibility of a data scientist. Financial models, Business models, and Machine learning models are some of the models which are developed by different types of data scientists. 

    List of 15 Different Types of Data Scientists

    Data science is a broad term; the number of activities involved in data science is classified among professionals called Data Scientists. Let's explore the different types of data scientists along with their responsibilities.

    1. Machine Learning Experts

    The advancement in technology over the years has increased the artificial intelligence and decision-making capabilities of modern-day computers. Machine Learning professionals are responsible for creating algorithms and delivering outcomes by deriving patterns from big data inputs and past trends. 

    They are used to produce an outcome, such as pricing strategies, products, derived patterns, etc. They help to develop innovative data science model types, which in turn help in resolving business challenges. To sum up, Machine learning experts help the machine understand an underlying problem and train them to react to it using innovative techniques and algorithms. 

    2. Actuarial Scientist

    Actuarial Scientist uses mathematical and statistical models to assess risk in the financial sector. Apart from the above-mentioned skills, knowledge about BFSI (Banking Finance Services and Insurance) is mandatory. They predict the financial prospects, such as future income, revenue, and profit/losses of an uncertain event in banks or insurance companies. A prior experience in the finance industry is a must to become an actuarial scientist. 

    3. Mathematician

    In order to become a data scientist, a strong background in mathematics, particularly calculus, linear algebra, and statistics, is necessary. Today, businesses need mathematicians to join the commercial market as their intuition about mathematics can aid in numerous tasks such as interpreting losses in machine learning models, deriving probabilities about data, etc. Their expertise is of huge importance in optimizing and reducing the complexities of techniques used to combat data science challenges. 

    4. Business Analyst

    Data science is essentially used to help businesses to recognize issues with current processes and predict outcomes for future events. Different types of data science jobs are available in the market, with Business analyst being one of the top choices for data science aspirants.

    The business analyst helps boost a business's strength by making data-driven decisions. They use data to develop insights and suggest changes in organizations. They are liable for analyzing data using various tools such as SQL and Excel, compiling charts and graphs for data visualization, understanding business goals, and providing solutions based on past knowledge. 

    5. Data Warehousing Expert

    Data warehousing is a way of managing data. Experts in this field are involved in structuring and implementing data warehouse designs. These warehouses store a large amount of information consolidated from multiple sources. 

    They facilitate various business intelligence activities, especially analytics. Queries are applied to historical data, and decisions are made based on it. 

    6. Data Engineer

    Data engineers build systems that synthesize data for performing tasks and make predictions based on it. They collect the raw data from the data warehouses and convert it into information that can be interpreted by analysts. Data architecture designing is one of the fundamental roles performed by them. 

    They plunge into data and eliminate manual tasks using automation. This can help alleviate problems due to manual errors in the data. 

    7. Data Mining Expert

    A Data Mining expert looks into a large set of data and finds patterns in them using available software and tools. In other words, they recognize the hidden trends in the data using complex mathematical algorithms. Using these trends, they try to form assumptions about the data and learn about anomalies, and if there are any, this facilitates applying the correct statistical method for validating predictions about an event. 

    8. Data Visualization expert

    A data visualization expert takes in the raw data and presents it in the form of graphs and charts for interpretation. This way of presentation serves as an excellent way of making the non-technical audience familiar with the technicalities of the data. Tableau and PoweBI are some of the most widely used data visualization tools. Python consists of various libraries which help in data visualization purposes. They help eliminate outliers and give the exact idea of how scattered our data is. 

    9. Data Analyst

    A data analyst is responsible for gathering the data and interpreting it to resolve a particular problem. They turn the information generated from raw data into meaningful insights and convey them to the stakeholders. They compile their findings into reports which help in understanding customer needs. Their job can be divided into five parts: collecting the data, cleaning the data, modeling the data, interpreting the data, and presenting the finding of the data. 

    We can find data analysts in almost all sectors Health Care, Food, Technology, Fashion, environment, etc., under various names. An immense amount of data is generated every day in each industry; thus, taking benefit of it with the help of data analysts is what stakeholders are looking for in today’s world. 

    10. Cybersecurity Data Scientist

    Cybersecurity Data Scientist help in the prevention of fraudulent activities. They develop data science models trained on past data to predict the likelihood of an intrusion or attack. This field of data science involves developing algorithms to deduce patterns from previous attacks and beforehand warning about the reliability of the system in use. 

    The need for Cybersecurity data scientists has increased tremendously with the rise in challenges related to security. Risk analysis should be one of the primary skills possessed by a data scientist cyber security expert.

    11. Spatial Data Scientist

    The increase in the usage of GPS-based systems has given rise to a new vertical in the field of data science. The data produced from GPS systems are a lot more complex than the normal scaler data; they need special handling, processing, and mapping. They have special database management system requirements. The question arises in which industries do spatial data scientists find themselves. One such application we find in the meteorological department. They take the help of these experts to analyze the satellite images and do daily weather forecasting. There are many more sectors, such as irrigation, fertilization usage, etc., where inputs from these professionals are of extreme importance. 

    12. Data Quality Analyst

    A Data Quality Analyst keeps a check on the quality of the data from which an organization makes an informed decision. They resolve data quality problems and increase the integrity of the data. Proficiency in understanding data along with knowledge about analytical tools is required to make a career as a Data Quality Analyst 

    13. Digital Analytics Expert

    Digital Analytics Experts or famously called Digital Analytics Consultants, are a primary need of almost all companies, be it MNCs or small-scale startups. They are professionals who are not just sound in technical intricacies but are also equipped with business and marketing skills. They collect data from different websites using JavaScript and inject them into analytical tools such as Google analytics. The data injected is further visualized, and dashboards are created for monitoring purposes. This entire process involves processing and filtering data in ways that satisfy the business needs of the enterprises. 

    14. Software Programming Analyst

    Software programming analyst specializes in using programming languages for performing system analysis. Knowledge about any programming language, i.e., Python or R, is crucial. Programmers help in automating data-related tasks, thus playing a major role in optimization and reducing computational expenses. 

    15. Cloud Computing Expert

    Cloud computing has proved to be one of the biggest game changers in the market. It facilitates various companies by providing services such as databases, storage, data analytics, etc., over the internet. Data scientists are responsible for collecting and analyzing data stored in cloud databases. Organizing and managing data at the proper storage locations is also a part of their job. 

    If Cloud computing is not used in data science, the data scientist will have to store data on servers, and for every data analysis task, they will have to transfer data from the primary servers to their local system, which is computationally very expensive. It also increases the hardware requirements for both the primary servers and the systems on which data analytics jobs are performed. 

    Since the entire world has switched to digital platforms for working, taking up an online Data Science Bootcamp can also serve the purpose. 

    Data Scientist Salary and Job Outlook

    By now, it is no surprise that the types of data science problems businesses are encountering have led to a major surge in different types of data science jobs. This demand is not going to stop anytime soon. A data scientist’s salary is completely dependent upon which vertical of data science he is involved with. The average salary of a data scientist is $100,560, according to the U.S Bureau of Labor Statistics. There are various data science job types, and the salaries for each one of them vary, but they remain close to the above-stated average. The growth rate for this sector of the industry is going to increase exponentially in comparison to other areas.  

    Tips For Becoming a Data Scientist

    There is no one pathway to becoming a data scientist. Everyone follows a different trajectory to hail as a data scientist, but here are some tips which can make this journey easy.

    Steps to Become A Data Scientist

    1. Having an academic degree or taking up appropriate courses

    In order to pursue a career in data science, having a degree in any of the following disciples, i.e., mathematics, linear algebra, statistics, computer science, or information technology, is preferred. Statistics, Programming, and mathematics together form the heart of data science; hence having a strong foundation in these areas is extremely necessary. If someone does not have an academic degree in the above-mentioned areas, they can take up some type of data science courses and establish themselves in this field. 

    2. Exploring and choosing the right role

    From the beginning itself, it is difficult to figure out a role that fulfills our interests or utilizes our complete potential. Hence, exploring different areas in artificial intelligence and doing different types of data science projects would help in finding the best fit for oneself. 

    3. Learning a Programming Language

    Learning Python or R programming language can help aspirants in getting an edge over their competitors. Software programming skills benefit in performing data-related activities smoothly, much more quickly, and efficiently as compared to manual intervention. 

    4. Gaining practical experience

    All said and done, and it is impossible to get a hold of data science by just theoretical abilities. Dealing with real-time industry challenges is an experience in itself, so gaining some hands-on experience by doing an internship or traineeship is a plus point. 

    5. Basic database knowledge

    Data is stored and managed in databases, queries are applied, and the resultant outcome helps in making a decision further. Thus, knowing about any databases like SQL can ease a lot many problems; it gets added as a feather in a hat full of skillset.  

    Check out KnowledgeHut’s best Data Science certification, It has an elaborative curriculum on the constitutional idea of data science, and it provides a lot of clarity to data science practitioners.


    In the end, it is just a matter of choice; if you intend to pursue any of the above-mentioned professions, make sure you are thorough about the responsibilities that come along and the skillset requirement for each one of them. As a data scientist, awareness of all these experts is a must. All types of jobs in data science have a bright future and will yield fruitful results. 

    Frequently Asked Questions (FAQs)

    1What are the 4 components of data science?

    The four components of data science are data warehousing, data engineering, data visualization, and data analysis.

    2What type of work is data science?

    Data science involves working on data collected over a period of time and analyzing trends on it to predict future possibilities of a similar event in an organization. 

    3Which is the best field in data science?

    Every field has its own importance, and each one is different from the others; hence it is difficult to compare and comment on which one is better.

    4What are the types of data science?

    There are 15 types of data science specializations. Anything apart from this is just a subset. 


    Heer Shah

    Blog Author

    Heer is an AI enthusiast and her interest lies in Data Science, Machine Learning, Deep Learning, and Computer Vision. She enjoys developing ML models for real-time challenges possessed in the world. Working with a large amount of data and deriving insight information from the same is her expertise. Heer is a Potterhead, an avid reader, and loves coding in her free time. She can speak six languages and enjoys writing about technology.

    Share This Article
    Ready to Master the Skills that Drive Your Career?

    Avail your free 1:1 mentorship session.

    Your Message (Optional)

    Upcoming Data Science Batches & Dates

    NameDateFeeKnow more
    Course advisor icon
    Course Advisor
    Whatsapp/Chat icon