10 Types of Data Scientists in 2025

Introduction

Woolgathering.

The term is used to describe indulgence in aimless thoughts or dreams. Specifically, I was woolgathering my entire childhood to aspire to become a scientist.

Yeah, the one shown in fancy cartoon movies - stargazing the entire night to find celestial bodies, mixing chemicals, or maybe launching a rocket in the open space!

That sounds weird, right? I was too overwhelmed by the word ‘scientist’, so much so that I didn’t even know that a ‘scientist’ is not a generic role but a specialisation in a specific domain that requires deep expertise.

Alright, let’s come back to reality. Now, specifically speaking about India, let’s do a basic Google search around - which scientist role is best in India.

The only screaming result that Google will show you is Data Scientist. And it’s true. Whether on the salary or demand front, a data scientist is among the top roles in the science domain, geared up for exponential growth.

According to Mordor Intelligence, the total market size for Big Data in India will reach $2.17 Billion in 2024 and grow to $3.38 Billion by 2029, growing at a rate of 7.66% CAGR. (Mordor)

According to a NASSCOM report, roughly 200,000 data science professionals are in India, and this demand is expected to increase. Some estimates suggest that the country will require around 1.5 million data professionals by 2025.

Before diving deeper, it’s important to understand the types of data scientists and professionals. This blog will explore the 22 types of data scientists alongside their major roles and responsibilities.

Reading this blog will give you a clear roadmap on the discipline of data science and which career path to opt for.

Without much ado, let’s begin!

What is Data Science?

Much needed - let’s hit the basics first! Let’s try to understand data science in layman’s terms. Let’s go back a few centuries. Imagine when the Mughals, Mauryans, or the Delhi Sultanate ran the country.

Can you take a rough guess on what was needed to win a war back then? Correct - weapons, army, and more weapons! Times evolved, the British came and went, and fast forward to 1947, India saw its first Government being formed under the leadership of the country’s first Prime Minister, Jawaharlal Nehru.

Now, fast forward to 2024 - can you take a guess what is needed to win a war in today’s date? Information, Foreign Intelligence, and much more - the one you see in RAW movies!

So, why am I speaking about RAW here? What’s the connection between RAW and data science? Data is the new-age weapon of today that is needed to succeed in any business.

From Instagram reel views to online sign-ups for a webinar, email open rates, and even the revenue generated from a single influencer activation - we are surrounded by data!

Optimizing data in a useful and resourceful way customized to a particular business’s needs can do wonders! Here comes the role of data science.

Data Science is a multidisciplinary field that uses raw data to extract knowledge and solve problems by turning them into data-driven, actionable insights.

Data science combines mathematics, statistics, specialized programming, and advanced analytics, backed by Artificial Intelligence (AI) and Machine Learning (ML) algorithms that convert raw chunks of data into useful and actionable information.

In today’s world, data science has climbed up the ladder to occupy the topmost priority for any business that wants to thrive in today’s competitive landscape.

Who is a Data Scientist?

Already feeling overwhelmed? Well, data science is a field that can leave you overwhelmed with its vast array of use cases. So, the question of the hour is, who is a data scientist, and what do they do?

A data scientist is not an astronaut! They are just extremely well-versed in understanding and playing around with data. A data scientist is an individual who uses raw chunks of large data and extracts information from them to help organizations make strategic decisions.

Does the company need to hire more people in the tech team? How much ROI is generated against the base investment? What form of trial and error has rendered the best result? Should the company focus more aggressively on influencer marketing?

All these questions are derived from one core solution - Data. Hence, you can understand the importance of data.

Data Scientists use a wide canopy of techniques, tools, and technologies to analyze data, backed by Artificial Intelligence and Machine Learning. They excel in translating complex information into easily understandable visual representations, making it easy to decide.

Again, a data scientist is not a one-man role; they work closely with data engineers, data analysts, and business analysts to make things work.

Types of Data Scientists - Understanding Major KPIs

Here we go! Buckle up as we dive deeper into the fascinating world of data science, explore the different types of data scientists and understand their major roles and responsibilities, alongside the skillset you need to be one of them.

1. Data Scientist

Let’s start from the top of the funnel - Data scientists. So what do they do? First, let's hit some official stats!

India earns 60% of its data science market revenue from exporting data science expertise to the United States. The United States Bureau of Labor Statistics projects that data scientist jobs will grow 36% from 2023 to 2033, much faster than the average of all occupations.

We have already hit the ground on how big the data science industry is and how it has clear grounds for exponential skyrocketing growth!

As mentioned, a data scientist is an experienced individual who works closely with analysts and engineers to extract actionable insights from large chunks of raw data and convert them into easy-to-understand diagrams, graphs, and charts for visual representations of trends and forecast predictions.

Next, they use this information to create and train AI models to forecast future trends, identify existing business patterns, and suggest projected patterns to drive business growth and scalability.

Data Scientists are also responsible for effectively training LLMs (Large Language Models) and explaining why a specific output set is generated (Explainable AI or XAI).

Cutting short, the sole responsibility of a data scientist is to research and study large pools of unstructured data and then plan and categorize the information in a format that catalyzes and streamlines strategic planning and decision-making.

2. Data Analyst

You might have never encountered a big MNC or a tech giant that does not have a team of data analysts! Data Analysts are a crucial asset of any organization, especially for those who use a large amount of data daily.

Data Analysts are primarily responsible for converting the information they extract from raw, unstructured data into graphical representations that can be conveyed to stakeholders for business decisions.

They are primarily concerned with collecting and cleaning data for duplicates or errors, analyzing data and identifying patterns that can be leveraged to drive business growth.

So, is there a difference between a data scientist and a data analyst? An absolute yes! Data Analysts typically help organizations make strategic, informed decisions using tools like Excel and SQL.

On the contrary, data scientists create sophisticated models that can predict future trends and patterns (by leveraging the charts and reports created by data analysts) and require excellent proficiency in programming languages like Python and advanced ML techniques.

The core difference between the two lies in the fact on the tasks they perform, the skills they use, and the level of education/expertise they have.

3. Data Engineer

Now, data engineers work on something without which the role of data scientists and analysts would become nullified - Accessibility.

Data Engineers are primarily responsible for building systems that collect, manage, and convert the raw data into usable information that analysts and data scientists can interpret.

They are responsible for building and maintaining data management systems to help businesses access, store, and process data more effectively.

Their holy grail is to make data more accessible to stakeholders so it can be used to assess and evaluate current patterns and optimize performance.

4. Machine Learning Engineer

It is one of the world's most high-paying yet complex jobs! Machine Learning Engineers are a crucial and integral part of the data science team.

You might already know that data scientists are responsible for creating AI models for predictive analysis. But how are the models trained to give accurate predictions?

There comes the role of ML Engineers! Machine Learning Engineers are responsible for designing and developing artificial intelligence and machine learning software to automate predictive models.

They are typically involved in building and training AI models that can learn and predict by themselves (this involves 4 types of Machine Learning - supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning).

Their core job responsibilities lie in developing artificial intelligence for machine learning and maintaining and improving existing AI systems.

ML Engineers manage the entire data science pipeline, from sourcing data to training models. They deploy models to production that can serve real users (e.g., ChatGPT, Gemini, etc.).

They are also responsible for evaluating model performance using metrics such as accuracy, precision, recall, and the ROC curve. They also detect and solve model overfitting and underfitting.

5. Data Warehouse Engineer

A Data warehouse engineer is responsible for designing, building, and maintaining the infrastructure that stores the data. In short, they are solely responsible for maintaining the ‘warehouse’ that collects, stores, and analyzes data.

Data Warehouse Engineers manage the entire back-end development lifecycle for the company’s data warehouse, including implementing ETL processes, building database cubes for performance management, dimensional design of table structure, and more.

They ensure that the systems function optimally over time and the efficient retrieval, processing, and analysis of large data sets.

They use tools such as UNIX shell scripting and PL/SQL, as well as big data tools like Hadoop, MongoDB, and Kafka, and they usually present data in XML formats for standardization. Data Warehouses are crucial for Business Intelligence (BI).

6. Operations Research Analyst

Operations Research Analysts might be quite infamous, but they are one of the major pillars of data science. Operations Research Analysts manage and evaluate data to improve business operations, pricing models, and marketing.

Their responsibilities include identifying problems in core business functions such as business processes, logistics, supply chain, etc. and leveraging statistical and mathematical modelling to improve processes and make better decisions.

They collect rigorous data from various sources, including databases, sales history, customer feedback, etc. Next, they use statistical and modelling techniques to extract relevant information and analyze the data.

Finally, they develop mathematical models to represent the problems, test them to ensure they work accurately, and reformulate them as needed.

Some typical examples of an operations research analyst include organizing products at the supermarket, scheduling flights and airline ticket prices, offering sales at the best times of the year, etc.

These need meticulous research and optimization to increase sales and revenue while maintaining profitability and scalability.

7. Business Intelligence (BI) Analyst

As we slowly approach the last 3 types of data scientists, we uncover the most fascinating roles that data science offers. One of them is a Business Intelligence Analyst.

The term Business Intelligence quite defines the term for itself. A Business Analyst gathers, cleans, and analyzes data collected from various sources, including but not limited to revenue, sales, market information, customer engagement, etc.

In layman’s terms, business intelligence analysts are responsible for analyzing all the core metrics that keep the business up and running optimally and using that data to uncover insights that help stakeholders make better business decisions.

After gathering and analyzing the data, they use data analysis to identify trends and relationship patterns.

Then, BI Analysts use the insights derived from their analysis to make recommendations that can help optimize and improve business processes to drive growth.

BI Analysts may also create comprehensive reporting dashboards to present their findings. They may also program data models to visualize or monitor data in real-time.

8. Data Quality Analyst

As the name suggests, a data quality analyst is an individual who is responsible for the quality of data and ensuring that data sets are accurate, reliable, and usable. Any discrepancies in data can prove to be extremely fatal and can put the entire business process on hold.

Therefore, ensuring data is reliable and accurate is crucial for any business, and a data quality analyst fulfils this very role.

This is primarily achieved by using data profiling tools to identify flaws in the data production process, evaluate the flagged flaws in terms of their seriousness, and prioritize problems for review.

Data Quality Analysts are also responsible for addressing inconsistencies and inaccuracies in data and creating data quality standards and quality procedures.

Data Quality Analysts work closely with business owners and stakeholders to ensure data integrity, resolve data quality issues, implement a data quality strategy, and improve overall company reporting.

Last but not least, they also evaluate existing system enhancements to improve data quality.

9. Data Architect

Second last in the spot comes yet another exciting role - Data Architect. A data architect is an experienced IT professional who analyzes and reviews an organization's data infrastructure, designs and plans future databases and implements scalable solutions to store and manage data.

The primary role of a data architect is to design and manage an organization’s data framework, ensuring that data is structured, stored, and easily accessible.

Data Architects work on creating and managing data models aligned with business needs, defining the way how data will be collected, stored, and accessed across systems.

They use the appropriate database technologies (SQL or NoSQL) and ensure they are designed and maintained for optimal performance. They are also responsible for designing frameworks to integrate data across multiple sources, facilitating seamless data flow.

Their major role is ensuring that the data architecture supports the organization’s goals and designing flexible and scalable systems that adjust to business growth and adapt to new technologies, assuring long-term sustainability.

Data architects also set standardized policies for data accuracy, security, and compliance in alignment with global legal regulations such as GDPR and HIPPA.

10. Cloud Computing Expert

With the large chunks of data that companies deal with and process daily, storing everything on physical servers is almost impossible. That’s where our last role in data science gets fulfilled - a cloud computing expert!

Cloud computing experts, called cloud architects or engineers, are experienced professionals who design, implement, and maintain an organization’s cloud infrastructure and overall strategy.

Their core responsibility is to collect and analyze data over a cloud database. They explicitly create cloud solutions, including existing database architecture and components.

Additionally, their responsibilities also involve but not limited to deployment and migration of cloud solutions, as well as monitoring and optimizing cloud solutions to ensure reliability and optimal performance.

Last but not least, they also design and implement security measures to protect breaches of cloud assets, viz. Encryption and identity management.

Takeaway

I hope I could clear the clouds on data science and explain to you what exactly data science is, who is a data scientist, and bifurcate the roles of the 10 major types of data science roles.

As the saying goes, not every person who looks rich is a millionaire; some are just professional actors. In the same way, not every person involved in a data science role is a data scientist or a data analyst.

This blog is your stepping stone into understanding the world of data science, the major roles that the industry provides, and the responsibilities associated with each.

If you have read the blog completely, you would, by now, have a clear understanding of the data science roadmap and what skills to get equipped with if you are looking forward to having a career in this broad industry.