With the rapid growth of data science, machine learning (ML), and artificial intelligence (AI), many new and recent graduates see these careers as exciting opportunities. The innovation that surrounds these technologies, like the use of recommendation systems, fraud detection, self-driving vehicles, and smart businesses, continues to increase as more and more Companies are basing their decisions on data. As Companies utilize data-driven decision-making, they are seeing a tremendous increase in the demand for skilled, professional data workers.
Before applying to a data science or ML program, the most frequent and sensible inquiry a potential applicant has is “What will my salary be when I begin working?” Given all the opportunities available in these fields, salaries are extremely dependent on skill level, practical experience, geographic location, type of industry, and organization. Regardless, as we move into 2026 and beyond, entry-level positions in this area will still be compensated competitively; however, candidates with considerable practical experience will likely earn significantly more than their peers. Below, we will examine salary expectations, influencing factors, and future salary potential in detail and in an easy-to-understand format.
- Entry-Level Salary Range in India (0–2 Years)
In India, the typical range for the salary of someone who has just graduated entering into a Data Science / Machine Learning (ML) role as an “entry-level” professional is as follows:
Entry Level Salary Range:
- Average Salary Range for Entry-Level Data Science/ML Roles – ₹5,00,000 – ₹8,00,000
- Salary Range with Strong Technical Skills / Internship Experience ₹10,00,000 – ₹14,00,000 for Candidates who have Completed an Internship Program in Data Science/ML
- Highest Starting Salaries for Candidates that are from a Premium Institute (IIT, IIM, etc.) or have a strong project portfolio can exceed ₹10,00,000
- What Roles Freshers Commonly Get
Recent college graduates in the Data Science and Machine Learning field typically find employment in one of the following areas:
Junior Data Scientists or Junior Data Analysts, where they will perform exploratory analysis and create dashboards based upon their findings.
Machine Learning Engineers (Entry Level), where they build and tune Machine Learning models for deployment into Production Systems.
AI Engineers (Fresher Positions) and assist with the Development of Artificial Intelligence Applications.
Analytics Consultants/Business Intelligence Analysts will help translate and interpret the Business data that the Company needs to make informed decisions about the Business.
The salaries associated with each of these positions vary based on the individual’s experience level, with ML Engineers typically starting at 5–11 Lakhs Per Annum (LPA), and Data Scientists typically starting at 6–14 LPA.
- Role of Company Type & Location
The following is a general description of Companies by size and sector.
Generally speaking, larger multinational corporations and product-based companies offer higher starting salaries to new hires, compared to smaller consulting or mid-level firms.
Technology hubs such as Bangalore, Hyderabad, Pune, Mumbai, and NCR are also generally higher paying than cities of similar size.
Types of jobs in Demand:
Most data-related positions (for example, Data Scientist, Data Analyst, Data Engineer) in the areas of Financial Technology, Cloud-Based Software as a Service, E-Commerce, Health Care Analytics, Artificial Intelligence Research, etc., typically have higher starting salaries than in other sectors.
Typically, Companies who utilize massive amounts of data and Machine Learning generally have structured hiring programs specifically for new graduates and will pay a slightly higher than average salary to their new employees.
- Comparisons With Global Salaries
The primary concentration is India; however, it is important to provide context on entry-level Data Professionals globally and how there are some countries, like the United States and many countries in Europe, that pay entry-level Data Professionals at a significantly higher salary level, averaging over USD 80,000+ annually for technology and AI-related jobs. It should also be noted that the cost of living and availability of jobs are not the same everywhere.
Freelancing options from abroad or overseas clients are also widely available to fresh graduates that will pay more than local job packages.
- What Influences Fresher Salaries Most
Many different elements will contribute to how much you will earn when you enter a job:
Projects/Practical Experience
Your real work experience through projects, internships, and other types of work you’ve done (portfolio) demonstrates your readiness to accept an offer and could increase your total value.
Technical Skills
Having the ability to program in Python and SQL or database access with SQL, as well as languages like R for data manipulation, programming languages like TensorFlow and PyTorch for creating machine learning models, etc.
Quality of Courses and Certifications
Having recognized credentials (certifications) with hands-on components adds significantly to your credibility.
Knowing the Business Domain
Knowing what problems an industry is trying to solve with data (for example, healthcare analytics with data sets of patients’ conditions or how finance works with data) positions you to succeed in those industries.
- Tips to Improve Your Fresher Salary Offer
Here are 4 Steps you can take to enhance your entrance-level salary as a Data Scientist or Machine Learning Developer:
working on real-world data sets, completing a Capstone Project, and/or donating to open-source projects is probably going to be much more valuable to a potential employer than any certificate.
Internships will give you a good idea of what it’s like to work at your potential employer; Interns also typically become full-time employees, and companies often offer better jobs to convert interns to full-time status.
Companies are looking for people who can immediately begin to use technology to do their jobs. So, if you know how to program in Python, use SQL, work with machine learning libraries, use cloud computing platforms, and use data visualization software, then you’ll be in a good position to get hired quickly.
Communication skills can make you a stronger candidate than all of your technical skills combined. By being able to clearly articulate your insights, you’ll be able to stand out from the crowd of other candidates who may have all the technical skills but no communication skills.
- Impact of Certifications and Specializations
Certification and specialization are crucial in determining salary potential and career opportunities for entry-level professionals in the competitive arena of Data Science, Machine Learning, and Artificial Intelligence (AI). While a bachelor’s degree / general education provides the foundation of core principles, certification provides validation of one’s abilities. Employers look for individuals who have completed structured programs, demonstrating to them that the individual is trained, tested, and ready for employment.
By 2026, companies will be looking for more than just “knowing data science”; they will want candidates with skill sets that focus on a narrower and higher-demand specialty. For instance, certification in one of the following areas: Machine Learning, Deep Learning, Natural Language Processing (NLP), Computer Vision, Big Data, or Cloud-based AI solutions will give entry-level professionals a significant advantage over those with general knowledge.
The reduction of hiring risk from an employer’s perspective creates significantly fewer headaches for the employer. In most cases, employers will give a higher starting salary, quicker onboarding, and an increased level of project responsibilities to the individual who completed a structured programme and better understands the standards, tools, and processes used in the data science workplace. Many recruiters will only consider applicants with a certification prior to evaluating their experience for entry-level positions.
- Importance of Soft Skills in Salary Growth
The primary focus for new professionals who want to advance their careers in the fields of Data Science and Machine Learning (ML) should be to develop soft skills. Soft skills are defined as the ability to interact and communicate with others; they are important because they allow a person to present his or her data in a way that is understandable by people who have little or no technical background. It is important for professionals in Data Science and ML to develop strong teamwork and problem-solving skills as they will be required to work on actual Projects. In return for these skill sets, employers will generally offer better wages and quicker chances for promotion to new professionals. In addition, soft skills such as interpersonal communication, coupled with technical skills, will enable many more professionals to assume leadership positions and increase their earning potential over time.
- Job Satisfaction vs Salary Expectations
Although salary is a critical factor at the beginning stages of a data career, job satisfaction will help determine long-term success. Roles that provide opportunities for learning, mentoring, and actual involvement in projects will help new employees advance their careers more quickly. Many employees will accept lower starting salaries in return for having the opportunity to develop skills and expertise. Over time, employees who have developed their skills and talents will have access to better positions, higher salaries, and more satisfying careers.
Conclusion
Fresh graduates can expect to find many Career opportunities in Data Science, Machine Learning, and AI through 2026, with many companies offering competitive starting wages combined with substantial Room for career advancement. Starting pay rates for Fresh Graduate employees will be different based on Employee Skill Sets, Project Types, and Hiring Company Types. Candidates who possess Relevant Certifications and Hands-On Experience Stand Out from the Competition when they Seek Employment Within the Job Market. It is more important to focus on practical learning, Putting Yourself in an Environment of Continuous Skills Development, and Creating Real-World Experiences Than Simply Seeking a High Initial Salary Package. Consistent Efforts Combined With the Right Attitude Will Enable Fresh Graduates to Quickly Grow Their Careers, Increase Their Salaries, and Provide Them with Long-Term Job Security in a Highly Competitive Industry.