The technological landscape is changing quickly, and both students and working professionals are asking the question: Is Data Science and Machine Learning (ML) a strong and sustainable career path over the next decade? As more and more data is created and the amount of new technology develops with Artificial Intelligence (AI), many companies are going through digital transformations, so careers using data to help guide business decisions are among the most sought-after careers available. This article outlines what is going on with Data Science/ML today and looks at what will happen next, including trends, key skills employers are looking for, potential salaries, industry growth, and useful information about why (or why not) you should pursue a career in Data Science/ML until 2035.
1. Why Data Science & Machine Learning Matter Today
Data Science and Machine Learning are critical technologies that enable businesses to use data to drive their decisions by automating and predicting their future behaviors and driving innovation within their businesses.
Today, businesses generate a large amount of data from different sources that reflect how they are performing. For example, businesses might create large amounts of customer-based data (customer behavior) and store this information in databases, market analysis, operational analysis, etc. This raw data has little value without the ability to interpret it, transform it into insights, and act upon those insights.
The role of Data Scientists and ML Engineers is to extract value from all of the data that companies have to help them to create insights and predictive models that help solve problems.
There are several ways in which these two roles contribute to companies:
Decision Support – Companies leverage data based on their insights to support key business decisions (finance, health care, retail, logistics, etc.).
Automation – ML models can automate repetitive tasks and significantly increase the ability of businesses to manage massive systems.
Competitive Advantage – Businesses that leverage data to develop business models, know what is happening in their industry, and quickly respond to changing markets outperform those that do not.
Data science is more than simply a means of “managing” data; it is a means to provide businesses with the ability to leverage their data to generate business intelligence for innovation and growth potential.
2. Current Industry Demand & Job Growth
Current studies and employment forecasts demonstrate a continuing increase in the need for data-based experts in an increasing number of sectors.
Industry job growth trends have demonstrated that various types of Data positions are available across multiple types of businesses, such as Finance, Health Care, E-commerce, Energy, Consulting, and Government Digital Services. By performing digital transformation, businesses have a growing need for individuals who can analyse data to generate strategic direction based on data interpretation.
In fact,
Data Science job openings indicate a growing number of Data Science positions now require machine learning competency. Approximately 77% of Data Scientist job postings in 2025 will require ML competencies.
Data Science and ML Specialist salaries have not only remained competitive with other traditional technology job salaries, but they also command a premium compared to many other technology job salaries.
The three components of Cloud, AI, and Data Analytics constitute the digital capabilities of almost all corporations.
Globally, related technology and analysis job growth projections demonstrate a faster-than-average rate of growth compared to other job growth rates. This trend will continue for at least the next ten years.
However, it is important to understand that demand for these job categories varies by geographic region and industry, with the highest job growth occurring in technology-based countries such as the United States, Europe, several Asian countries, and the faster digitising economy countries.
3. Job Roles Within Data Science & ML
The field of data science encompasses several different areas of specialization; these specializations have different types of focus areas:
a)Data Analyst
Data analysts are responsible for analyzing data sets and providing written reports that assist businesses in understanding their data and the trends associated with that data.
The most common tool used for this type of work is Excel (like Microsoft Office). Data analysts can also use SQL and Power BI or Tableau for data visualizations.
Data analyst salaries usually start off fairly moderate and increase as one gains experience and develops new skills.
b) Data Scientist
Data scientists build predictive (statistical) models and perform statistical analysis on a variety of sets of data. They primarily use programming languages such as Python or R and have experience working with the concepts of machine learning.
The need for data scientists is growing rapidly in industries such as e-commerce, healthcare, and finance.
c) Machine Learning Engineer
Machine learning engineers design, build, and deploy machine learning models to production environments. They combine software engineering knowledge with analytical skills to achieve these goals.
Demand for machine learning engineers remains strong in 2025’s job postings, with salaries falling in the higher end of the range due to the continued growth of this industry.
d) AI/ML Research or specialist positions
These positions focus on conducting research related to advanced forms of artificial intelligence and natural language processing, building deep learning frameworks, and developing state-of-the-art technological innovations.
While the other specializations also have their own unique requirements regarding skills and salary potential, they are all linked by their underlying goal of using and leveraging data to assist businesses in improving their bottom line.
4. Salaries & Compensation Trends
Data Science and Machine Learning careers are among the highest-paying careers in India today due to the increasing need for data-driven decision-making and AI skills in a variety of industries.
For New Hires (Entry Level)
If you are new to the field or have just recently completed your education in Data Science / AI / ML (e.g., Entry Level), you should expect to earn between ₹4,000,000 – ₹800,000 with varying measures of success based on your skill level, certification, and project work.
Mid-Career Professionals (2 Through 5 Years Of Experience)
As a Data Scientist or Machine Learning Engineer who has experience working on real-world projects and has utilized tools such as Power BI, Tableau, cloud-based solutions, and machine learning frameworks, your salary will fall within the range of ₹8,000,000 – ₹18,000,000, depending on the type of company, type of work being done, and experience. If you are employed at a product-based business, startup,p or multinational corporation, you are likely to be compensated more towards the higher end of the scale.
Senior & Specialized Data / AI / ML Professionals
Senior Data Scientists, Machine Learning Engineers, and AI Specialists with niche expertise and ample experience deploying their work can earn between ₹20,000,000 – over ₹35,000,000; likewise, there are opportunities within some niche specialties where salary ranges could exceed this range.
5. Key Factors Fueling Growth Over the Next Decade
a) Increase in Data Volume
The amount of data being produced by organizations is large and continues to grow rapidly. This has created a need for more professionals with the skills to organize, interpret, and analyze the data.
b) The Use of AI and Automation
Due to the increase in the use of artificial intelligence across a variety of industries, there is a greater demand for data professionals with the knowledge and experience to effectively combine AI with the business and engineering issues facing those industries.
c) The Development of Cloud and Big Data Systems
The increasing availability of cloud computing and the emergence of big data technologies will require data professionals to become proficient in distributed processing, data lakes, and real-time analytics systems.
d) The Need for Ethical AI
As algorithms and machine learning programs are increasingly used to make life-affecting decisions, including decisions regarding credit acceptance and healthcare recommendations, the need for data professionals who have been trained in the areas of ethical AI, bias detection, and compliance is growing.
Based on these trends, it appears that the data science field will continue to evolve, requiring more specialized education and training and providing a greater variety of career opportunities.
6. Future Outlook: What to Expect in the Next 5–10 Years
The Changing Landscape of Data Professional Roles
The traditional definition of the role of the data scientist may shift to be more specific and specialised. With the introduction of AI tools, routine automation tasks will be performed by an AI, allowing data professionals to concentrate on the more strategic activities of framing problems, interpreting analysis results, and making decisions rather than simply constructing a statistical model.
Businesses Will Seek the Business Strategist in Data Analysts
More and more businesses will seek data analysts who can communicate their analytical findings or insights in relation to business strategy.
An example of a Collectively Created Skillset: The Future of Data Analyst Roles May Combine Data Engineering, Machine Learning Skills, and Domain Expertise (e.g., Healthcare Analytics), along with Cloud Architecture Knowledge.
With the rapid advancement of tools and analytics methods, professionals must continually develop new knowledge and skills in order to keep pace.
Conclusion
Yes, context is important.
Data Science and Machine Learning are among the fastest-growing and high-paying fields to be working in over the next 5–10 years and likely even longer! There is still a significant demand for professionals who are able to derive value from data and make better decisions with that information due to the expanding usage of AI/ML and automation technology, etc.
While the career opportunities remain exciting for many years to come, the job market for Data Science professionals is constantly changing and evolving. More of the repetitive tasks required to carry out their jobs are being fulfilled via automation tools, and therefore, companies require someone who has a hybrid skill set combining traditional “technical” proficiencies, as well as strong strategic capabilities with an understanding of how the business uses data to inform its decisions.
To succeed as a Data Science professional, you must build a true depth of skill, have relevant project experience, and continuously adapt to the latest technologies and shifting industry demands.
