9 Types of Data Scientists | Which One Should You Become?

There are many types of data scientists, each with a unique focus and skill set, including machine learning engineers, data engineers, business analysts, and cybersecurity specialists. The best fit for you comes down to what sparks your interest most, whether it’s predictive algorithms, business insights, or spatial analysis.

In this blog, you’ll explore what each of these roles involves, the key skills you’ll need, and typical salaries in India, helping you figure out which data science path is right for you.

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Popular Types of Data Scientists: Find Your Best Fit

Data science provides a range of career paths for data scientists, each with its distinct focus and requirements. As a data scientist, you could be building predictive models, managing data, or transforming it into actionable insights to inform decisions. Understanding these roles helps clarify the essential skills that guide you toward the data science career path that best aligns with your interests.

Strengthen your data science career in 2025 with online programs that build the skills employers look for. Explore upGrad courses to enhance expertise, boost confidence, and prepare for your ideal data science role.

• Master's in Data Science Degree
• Executive Diploma in Data Science & AI
• Executive Post Graduate Certificate Programme in Data Science & AI

Now, let’s explore nine different types of data scientists, each with unique roles and skills in the data science field.

1. Machine Learning Engineer

You’d build algorithms that learn from data and make predictions or automate decisions. It’s about training models on historical data and fine-tuning them to work well in the future. This role is common in companies working on recommendation systems, fraud detection, or predictive maintenance.

Key skills required

• Python or R programming
• Solid statistics and probability
• Familiarity with machine learning libraries (like Scikit-learn, TensorFlow, PyTorch)
• Data wrangling with SQL
• Ability to explain models to non-technical teams

Also Read: What is Predictive Analysis? Why is it Important?

2. Data Engineer

As a data engineer, you’d design and maintain the systems that store and move data. You’ll build data pipelines, clean raw data, and ensure everything is structured well so that data scientists and analysts can utilize it effectively.

Key skills required

• Strong programming (Python, Java, Scala)
• SQL and database systems (MySQL, PostgreSQL, MongoDB)
• Big data tools (Hadoop, Spark)
• Data warehousing concepts
• Basics of cloud platforms (AWS, Azure, Google Cloud)

Also Read: A Comprehensive Guide to Understanding the Different Types of Data in 2025

3. Data Quality Analyst

Here, you’d be the person who ensures data is clean, reliable, and accurate before it’s used for decision making. You’d track down errors, fix inconsistencies, and monitor data integrity.

Key skills required

• Deep understanding of data validation and cleaning
• Strong domain knowledge (knowing what “good data” looks like for your industry)
• Tools for data wrangling (Excel, Python libraries, Alteryx, Trifacta)
• Good problem-solving to trace data issues

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4. Business Analyst

In this role, you’d look at data to spot issues, trends, and opportunities that help your company make smarter decisions. You’d work closely with stakeholders to translate data into actionable insights.

Key skills required

• Solid grasp of business processes
• Data analysis with Excel, SQL, and sometimes Python
• Strong presentation and storytelling with data (using Power BI, Tableau)
• Ability to run what-if scenarios and forecasting

5. Digital and Marketing Analytics Expert

You’d focus on understanding user behavior, campaign performance, and customer journeys. Your job is to use data to help marketing teams spend smarter and improve engagement.

Key skills required

• Knowledge of web analytics tools (Google Analytics, Adobe Analytics)
• A/B testing and marketing attribution models
• SQL and basic scripting
• Comfort presenting data insights to marketing teams

Also Read: Best R Libraries Data Science: Tools for Analysis, Visualization & ML

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6. Cybersecurity Data Scientist

Your focus would be on using data to detect fraud, identify threats, and prevent attacks. You’d train models on past security incidents to predict and stop future breaches.

Key skills required

• Understanding of cybersecurity principles
• Python or R for anomaly detection models
• Machine learning for classification problems
• Ability to analyse logs and network data

Also Read: What is Linear Programming in Data Science: Overview

7. Spatial Data Scientist

If you enjoy working with maps or location data, this role is for you. You might analyze satellite images for weather forecasts or track geographic trends in retail or agriculture.

Key skills required

• GIS tools (ArcGIS, QGIS)
• Spatial data libraries in Python or R
• Data visualization skills to create 
• Statistical modeling of geographic patterns

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8. Actuarial and Financial Data Scientist

This role blends actuarial science with data science. You’d use models to estimate financial risks, predict profits or losses, and help insurers or banks plan for uncertainty.

Key skills required

• Strong foundation in statistics, calculus, and linear algebra
• Knowledge of the BFSI (Banking, Financial Services, Insurance) domain
• Tools like SAS, R, or Python for building risk models
• Experience with financial data sets

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Also Read: How to Become a Data Scientist – Answer in 9 Easy Steps

9. Data Visualization Specialist

You’d take complex data and turn it into charts and dashboards that others can easily understand. This helps teams identify trends quickly and make more informed decisions.

Key skills required

• Tools like Tableau, Power BI, or Looker
• Understanding of what makes a visual effective
• Ability to work with raw data (SQL, Excel, or Python)
• Communication skills to explain visuals to non-technical folks

If you’re wondering what these roles typically pay, here’s a quick look at the average salaries in India for these data science roles.

Source: Glassdoor

Here is the graph showing the average salary range (in LPA) for various roles in data analytics and machine learning.

Note:

• Salaries in top MNCs, startups, or product companies (such as Google and Amazon) can be significantly higher.
• Location (Bangalore, Gurgaon, Pune, Hyderabad) and domain (BFSI, e-commerce, SaaS) have a significant influence on salaries.

Also Read: Importance of Data Science in 2025 [A Simple Guide]

While salaries show the value of these roles, it’s the career path that tells you how to get there. Let’s find out how most data scientists build their careers.

What is the Career Path for Various Types of Data Scientists?

With nearly 11 million data science job openings expected in India by 2026, this field is experiencing rapid growth. Whether you're starting or planning a career switch, it’s a great time to enter. The core path for becoming a data scientist involves learning key skills, mastering essential tools, and building impactful projects to stand out.

Here’s how you can break it down step by step to build a career in data science.

Step 1: Learn the Core Skills

Develop a solid foundation in statistics to comprehend concepts such as distributions, probability, and hypothesis testing. Get comfortable with SQL for pulling data from databases, and pick up Python or R to manipulate, analyze, and visualize data.

Becoming proficient in tools like Excel, Power BI, or Tableau will also help you explore data quickly and create clear reports.

Step 2: Get Qualified

Choose between a university degree, a focused bootcamp, or flexible online certifications based on your time and budget. Each path offers different depth and structure, but all should give you solid technical knowledge and hands-on practice to prepare for real-world data work.

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Step 3: Work on Practical Projects 

Complete exercises and guided projects from your courses, then move on to your ideas. Select interesting topics, gather relevant data, thoroughly explore them, test hypotheses, and present your insights. Building end-to-end projects is one of the best ways to grow confidence and demonstrate real ability.

Also Read: 30 Must-Know Data Science Tools for 2025 & Steps to Pick the Right Ones

Step 4: Build a Portfolio

Collect your best projects, covering data cleaning, exploration, visualization, and even basic machine learning, and organize them on GitHub, Kaggle, or your own simple website. A clear and thoughtful portfolio demonstrates to potential employers how you think and approach problem-solving.

Also Read: Data Cleaning Techniques: 15 Simple & Effective Ways To Clean Data

Step 5: Apply for an Entry-level Job

Tailor your resume for each job, highlight the key skills and projects that match the role, and keep your LinkedIn and GitHub polished. Connect with data professionals, join online communities, attend meetups, and stay active in conversations around data to open more doors.

Step 6: Keep Advancing Your Expertise 

Explore specialized areas such as deep learning, natural language processing, or time series forecasting. Gain experience with cloud tools or big data platforms. Staying curious and continually updating your skills ensures long-term success and growth in the field of data science.

Also Read: Data Scientist Job Description – Job Guide

Whichever type of data scientist you decide to become, staying consistent with your learning and practice is what will truly set you apart and keep your career moving forward.

How upGrad Can Help You Become a Data Scientist?

The type of data scientist you should become depends on what problems you’re excited to tackle, be it predicting trends, spotting security threats, or telling stories through data. Each path needs a strong base in statistics, programming, data tools, and clear communication.

With upGrad, you can build these skills through hands-on projects and industry-focused learning. Their courses with top universities help you grow your expertise step by step, so you’re ready for the data science role that suits you best.

Here are some additional courses that go beyond general analytics to help you master areas like predictive modeling, NLP, and business strategy.

• Case Study using Tableau, Python and SQL
• Data Science in E-commerce
• Analyzing Patterns in Data and Storytelling
• Data Science in E-commerce: Pricing & Marketing Analytics
• Professional Certificate Program in Data Science and AI

Struggling to find the right program that aligns with your career goals to become a data scientist? Contact upGrad for personalized counseling and valuable insights, or visit your nearest upGrad offline center for more details.

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