What Is a Data Scientist? Salary, Skills, and How to Become One
Table of Contents
- Jaro Education
- 29, June 2025
- 11:00 am
Will you believe, if we say that approximately 2.5 quintillion bytes of data are generated globally daily? Well, honestly, that’s a lot to fill over 10 million Blu-ray discs. And more surprisingly, there is a fact that 90% of the world’s data has been generated in the last three years alone. Now you must be thinking, where does all this data come from, how is it being used, and who uses it? Well, the real heroes are skilled data scientists who can easily identify the available data and determine how they can gain insights to make an informed business decision.
So, if you’re the one who wants to become a data scientist, you’re at the right place. In this comprehensive blog, we will try to explain everything related to data science, including skills, salary, and how to become one and shine in the industry.
What Does a Data Scientist Do?
Data scientists are similar to detectives in modern times, except they are not solving crimes; they are finding the right information from data. What is the data scientist’s main job? Asking the right question and then figuring out how to answer it using data. Using this data, data scientists use their analytical abilities to build predictive models, which allow organisations to forecast outcomes and make better decisions.
So, what does a typical day look like for a data scientist? Let’s break it down:
- Identifying Patterns and Trends: Data scientists can extract patterns and trends from datasets. There can be a lot of data to sift through to extract the insights that will inform business decisions. Wouldn’t it be nice to identify a trend before the trend itself emerges?
- Creating Algorithms and Data Models: Data scientists build algorithms and data models using their programming skills to predict outcomes that may occur in the future. It is akin to developing a recipe where each ingredient is a different variable in the data. The better the recipe, the more likely you are to accurately predict the outcome of the dish!
- Utilising Machine Learning: Machine learning methods are revolutionising for data scientists; they can apply these processes to improve data or end products. It’s like teaching machines to learn from data, improve as they learn, without programming.
- Communicating Recommendations: Once insights are gathered, the data scientist needs to distribute those findings effectively. They can share recommendations with other teams and upper management, and they must interpret this complex data in a manner that the team can understand. Strong communication and data scientist skills are essential here, and data storytelling is an art!
- Deploying Data Tools: Data scientists are well-versed in various data analysis tools such as Python, R, SAS, and SQL. These tools help them manipulate data, perform analyses, and visualise results. Mastering these languages is crucial for turning raw data into actionable insights.
- Staying Updated on Innovations: The field of data science is ever-evolving, with new technologies and techniques emerging regularly. Data scientists must stay on top of these innovations to enhance their skills and apply the latest methods in their work. This commitment to continuous learning keeps their work exciting and relevant.
Exploring Data Science Jobs
As businesses increasingly rely on data to drive decisions, the demand for data scientists is skyrocketing. Here’s a look at some key roles in the data science field that you might consider:
- Data Scientist
The primary role, focusing on formulating questions, developing models, and making predictions based on data. - Data Analyst
Data analysts play a critical role in collecting, analysing, and visualising data. They use techniques like statistical analysis to identify trends that inform business decisions. - Data Engineer
Data engineers are the builders of data systems. They create infrastructure that automatically collects, stores, and manages data, making it easier for other professionals to analyse it. - Data Architect
Data architects design the blueprint for data management systems. They oversee the construction of these systems to ensure that data is organized and accessible for analysis. - Machine Learning Engineer
These engineers focus on designing AI programs that interact with large datasets. Their work is vital for developing intelligent systems that can learn and adapt. - Business Intelligence Engineer
Business intelligence engineers create and maintain systems that analyse data specifically for financial and strategic purposes. They help organisations make data-driven decisions that boost profitability.
Essential Skills Required for Data Scientists
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As we know, the job sector has become very competitive, and standing out requires a unique blend of skills. For data scientists, this means not only mastering technical competencies but also developing a keen analytical mindset and effective communication abilities. Here are the essential skills that can set you apart in this powerful field:
Technical Skills
IT professionals who are regularly working with computers can save themselves time and effort if they are aware when it comes to troubleshooting issues with some of these generic technical skills. So, given below are some of the most important data scientist skills that are in demand:
- Programming Language: A data scientist is required to be proficient in different programming languages to sort through, analyse and manage a large volume of data. Some of the most popular programming languages include R, SQL, SAS, and Python.
- Data Mining: This is another important technical skill that a data scientist must be fluent in to gather reliable data from multiple sources.
- Data Visualisation: A data scientist is responsible for creating charts and graphs. Thus, familiarity with multiple visualisation tools like Tableau, Google Charts, and Microsoft Excel to represent data in a readable way can help you gain business opportunities.
- Machine Learning: Once you’re aware of the fundamental concept behind machine learning, you can not only build predictive models but also be fluent in using frameworks like PyTorch, TensorFlow, and Scikit-Learn. These skills help data scientists to analyse patterns, predict outcomes and enhance data-driven business strategies.
- Data Engineering: Data engineering includes every data-related skill, from gathering data to wrangling and cleaning it — basically all the steps prior to analysis.
Soft Skills
Being a data scientist is more than a numbers game; it is a skill that requires a full toolbox! Let’s look at some essential skill sets that will make you a key player in this exciting and emerging profession.
- Sharing Insights: Data scientists often communicate insights with individuals less educated technically, therefore, it is very important to convey complex insights so they are understood. The analytical talent must be someone who has strong communication and presentation skills. This may be deemed as data storytelling!
- Analytical Thought: Data scientists use an analytical thought pattern to discover not only the right answers but also the right questions. An analytical thought with the right instinct allows data scientists to be inquisitive about the data to discover varying insights.
- Problem-Solving: Being a self-starter is a huge plus! Data scientists need to tackle problems head-on and continuously improve their problem-solving abilities. This proactive approach makes them invaluable team members.
- Adaptability and Flexibility: Data can be unpredictable, and sometimes it doesn’t reveal what we hope to see. When challenges arise, being adaptable and flexible allows data scientists to pivot and make the most of the situation, turning obstacles into opportunities.
- Collaboration and Teamwork: Data scientists rarely work in isolation. They thrive in teams, collaborating with colleagues from various departments to gather data and share insights. Strong teamwork skills are essential for success in this interconnected environment.
- Effective Communication: When issues come up—be it a project delay or a need for a strategic shift—clear and prompt communication is vital. It helps keep the team on track and ensures everyone is aligned with the project goals.
- Patience and Persistence: Believe it or not, sifting through data can be a grind! Sometimes, patterns don’t reveal themselves right away. Patience is essential for digging deep and uncovering those crucial insights that can make all the difference.
Salary of a Data Scientist as of 2025
There are different sectors, including IT, digital economy, machine translation, healthcare, medical, and big data where the expertise of data scientists is required. But their salary is dependent on various factors such as educational qualifications, courses, data analytical skillset, companies, and even locations.
In India, the average salary of a data scientist ranges somewhere between ₹ 4 to ₹ 4.5 Lakhs and goes up to ₹ 24.0 to ₹ 40.0 Lakhs as per their expertise and experience. Let’s have a look at data scientist salaries in different companies, based on their experience, and also globally.
Salary of a Data Scientist: Based on Experience Levels
Level | Experience | Average Salary Range (per year) | Common Job Roles |
Entry-Level Data Scientist | 0-2 years | ₹51,00,000 – ₹66,00,000 | Junior Data Scientist, Data Analyst |
Mid-Level Data Scientist | 3-5 years | ₹82,00,000 – ₹1,07,00,000 | Data Scientist, Machine Learning Engineer |
Senior Data Scientist | 5+ years | ₹1,07,00,000 – ₹1,45,00,000+ | Senior Data Scientist, Lead Data Scientist |
Lead Data Scientist / Manager | 7 + years | ₹1,24,00,000 – ₹1,66,00,000+ | Data Science Manager, Head of Data Science |
Popular Companies for Data Scientists in India
| Company Name | Avg. annual salary |
| TCS | ₹6.9 L/yr – ₹18 L/yr |
| Accenture | ₹7.2 L/yr – ₹26.6 L/yr |
| IBM | ₹12.6 L/yr – ₹36 L/yr |
| Cognizant | ₹8.9 L/yr – ₹26.4 L/yr |
| Capgemini | ₹4.8 L/yr – ₹19.5 L/yr |
| Deloitte | ₹6.8 L/yr – ₹28 L/yr |
| Tiger Analytics | ₹7 L/yr – ₹26.5 L/yr |
| LTIMindtree | ₹5.6 L/yr – ₹20.7 L/yr |
| Fractal Analytics | ₹8.4 L/yr – ₹31 L/yr |
| Wipro | ₹4.2 L/yr – ₹16 L/yr |
Steps to Become a Data Scientist
As it is said that there is no one-size-fits-all path, similarly, the answer to “how to become a data scientist” is also different. However, there are a few easy steps that can make your move easier:
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Step 1: Start with Any Preparation Course
If you’re new to this field, you can begin with an introductory prep course in data science. You will get a chance to learn about different tools and industries.
Step 2: Read on Your Data Science Fundamentals
If you’re thinking about diving into the world of data science, the first step is to take a good look at what you already know and what you still need to learn. Think of it as laying the groundwork before building your dream data castle! Mastering the basics is essential before you can explore the exciting niches and specialities that data science has to offer. So, let’s keep it simple and focus on some key areas to channel your energy:
- Statistical Analysis
- Programming
- Data Visualisation
- Machine Learning
Step 3: Meet the Educational Requirements
No need to worry! You don’t have to hold a college degree to begin your career in data science. However, there are still some milestones that you need to cover before getting that first data science gig!
- A Degree Is Not Required, But It Can Help: Degrees in data science are still new-ish and typically integrate math, computer science, statistics and business science. If you have a degree in any of the previous topics (or something else even related), then it is a good foundation for your career in data science.
- Take Certified Courses or Bootcamps: If you’re looking for a way into data science that does not involve getting a traditional degree, certified courses or bootcamps will be your best friends! One such popular programme is the PG Certificate Programme in Cyber Security Management and Data Science offered by IIM Nagpur through Jaro Education.
This online programme is designed for mid-management professionals, CTOS, CXO-level and senior managers to provide essential knowledge in the field of systematic risk assessment, cybersecurity, and team management.
Step 4: Master Essential Skills
Earning online certifications or working on multiple data science projects can help you to work on your skills and show your ability to different organisations.
Learn Data Science with Jaro Education
Without a doubt, gaining practical experience and advancing your education can help you increase the chances of becoming a highly paid data scientist. So, if you want to master the skills of a data scientist, consider registering in one of the popular programmes from Jaro Education, such as the PG Certificate Programme in Cyber Security Management and Data Science offered by IIM Nagpur.
Jaro Education is one of the leading online higher education and upskilling companies offering career counselling and academic guidance. We are here to bring industry-recognised online certification courses in collaboration with prestigious universities and schools. In fact, for various programmes offered by IITS and IIMS, we act as a marketing and technology partner.
In addition, for this programme, we offer Jaro Expedite – Career Booster.
- Resume Building: We meticulously create and refine resumes to meet corporate standards, showcasing your skills and achievements. Our expert optimization ensures your resume is ATS-friendly and stands out.
- LinkedIn Profile Optimisation: We provide tailored suggestions to enhance your LinkedIn profile, maximising visibility and fostering connections with industry leaders.
Career Enhancement Sessions: Through Jaro Connect, access immersive bootcamps and masterclasses led by experts, helping you stay updated on industry trends and sharpen your professional skills.
The Bottom Line
Whether you are at the beginning of your journey or want to move beyond being an entry-level data scientist (or analyst), understanding the skills and paths to success is essential. Data science has competitive salaries and diverse applications, making it not just a job but a dynamic career. Embrace the learning process, leverage available resources like Jaro Education, and, more importantly, know that your ability to leverage data can lead to significant change. The future is bright for data scientists – are you prepared to seize it?
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