Will AI replace Data Scientists, or will it change what Data Scientists do? As Generative AI and Agentic AI become more capable, organisations are rethinking how they collect data, conduct analysis, build predictive models, and make business decisions. Tasks that once required substantial manual effort can increasingly be supported by AI tools, changing the skills expected of Data Science professionals. However, the future of Data Science careers is not simply a story of human workers being replaced by machines. The World Economic Forum highlights the growing importance of AI skills alongside analytical thinking, judgement, and other human capabilities. McKinsey’s September 2026 research also found that reported AI-related workforce reductions had been lower than organisations had anticipated, although expectations of future reductions remained significant. For students considering a Data Science master’s in the USA, this shift makes interdisciplinary learning especially relevant. The MAS in Data Science USA Track combines Machine Learning, Artificial Intelligence, Big Data Analytics, Data Engineering, High-Performance Computing, and Data Strategy & Governance. These areas provide a useful foundation for understanding how Data Science education can adapt to an increasingly AI-driven workplace.
Will AI Really Replace Data Scientists in 2026?
While AI will most probably automate or speed up certain aspects of Data Science work, there is no proof yet that one can simply conclude the disappearance of the profession as a whole. AI algorithms are capable of helping with coding, data preparation, analysis, finding patterns, and reporting, which means that professionals will need to conduct their work differently. Nevertheless, the position of a Data Scientist involves more than just completing technical aspects. It is essential to consider the differences between the automation of tasks and the elimination of jobs entirely. Companies can reorganize their work processes based on AI technology, rather than getting rid of all those who use it. The report from McKinsey published in September 2026 showed that there were fewer people laid off from companies using AI technology than expected, while the expectations for the number of future layoffs were still high.
AI Can Automate Tasks Without Eliminating the Entire Role
AI can support several repeatable activities within a Data Scientist’s workflow. For example, AI coding assistants can suggest code, help with data transformations, support routine exploratory analysis, generate initial interpretations, and prepare draft reports. These capabilities may reduce manual effort and help professionals explore possible solutions more quickly.
Why Human Decision-Making Still Matters
Data Scientists not only analyze data but also define what needs to be analyzed, why, and how analysis outcomes will impact decision-making. AI may produce forecasts and suggestions, but experts need to consider whether such forecasts and suggestions make sense in the case of a certain business problem. The World Economic Forum’s June 2026 analysis highlights the importance of human contributions such as defining problems, setting constraints, evaluating outcomes, and making final decisions as AI becomes more capable. Implications for would-be Data Scientists are thus obvious – it is not enough to master AI technologies; there should be the ability to apply them correctly. The professionals who possess both technical skills and critical thinking could be ready to collaborate with more advanced AI technologies.
How Is Generative AI Changing Data Science?
Generative AI refers to AI systems that can create new content, including text, code, summaries, and other outputs based on user instructions and available context. In Data Science, these systems can assist with coding, data exploration, documentation, and analytical workflows. As organisations adopt AI-assisted tools, Data Scientists may spend less time on some repetitive tasks and more time reviewing results, refining approaches, and connecting insights to business objectives.
From Manual Work to AI-Assisted Workflows
Traditionally, Data Scientists have handled each stage of an analytical workflow, from writing code and preparing data to documenting findings and interpreting results. Generative AI can assist with parts of this process by suggesting code, explaining technical concepts, and producing initial drafts of analyses or reports.
Generative AI as a Productivity Tool
Generative AI could facilitate faster experimentation by enabling Data Scientists to try different methods, do repetitive technical tasks, and create first prototypes. Generative AI could also be useful for exploring new libraries, documenting, and testing hypotheses in early project stages. These advantages are not assured. They rely on the nature of the task, the AI solution quality, the complexity of the data, and the user experience. Data Scientists need to assess whether using AI brings actual productivity improvements or more verification work.
New Expectations for Data Scientists
As Generative AI becomes part of analytical workflows, Data Scientists need more than the ability to operate AI tools. They must understand how to evaluate AI-generated results, identify limitations, verify assumptions, and recognise when the underlying data is incomplete or unreliable. Important capabilities include AI literacy, data quality assessment, model validation, critical thinking, and broader business and domain knowledge. These skills help professionals determine whether an AI-generated result is appropriate for a particular problem and communicate its implications clearly. The goal is to combine AI-assisted productivity with the technical expertise and judgement required for reliable, responsible Data Science.
Why Is Agentic AI and Why Does It Matter for Data Science?
Agentic AI refers to AI systems that can work towards a goal by carrying out multiple steps, using tools, retrieving information, and coordinating parts of a workflow with some degree of autonomy. Unlike conventional AI assistance, which typically responds to an individual request, agentic systems can potentially plan and execute connected tasks with less continuous human input. For the future of data science careers, this development matters because AI is moving beyond generating code or summarising information towards coordinating parts of analytical workflows. The World Economic Forum’s 2026 workforce research identifies the growing use of AI, including agentic AI, as a factor that may reshape jobs and the skills organisations need. McKinsey’s September 2026 analysis also found that reported AI-related workforce reductions had been lower than organisations had anticipated, even as expectations of future reductions remained significant. These findings point to a changing work environment, not proof that AI agents will independently replace Data Scientists.
From AI Assistance to AI-Driven Workflows
Traditional AI assistance might help a Data Scientist write a query, explain a statistical method, or generate a draft report. An agentic system could potentially coordinate several connected steps, such as retrieving approved data, running a predefined analysis, summarising results, and flagging unusual findings for review. This shift could change how Data Scientists structure their work. Rather than manually managing every intermediate task, they may increasingly define objectives, connect tools, establish workflow rules, and review the outputs produced at different stages.
Why Oversight Becomes More Important
As agentic AI systems coordinate more steps, the consequences of an incorrect assumption or unreliable output can extend across an entire workflow. This makes oversight an important part of the future of data science careers. Data Scientists will need to check outputs, define constraints, manage data quality, review decisions, and ensure that automated processes remain aligned with business requirements. For example, an AI agent might generate an analysis using an outdated dataset or interpret a correlation as evidence of causation. Without appropriate checks, that output could influence a report or recommendation. Human review helps identify these issues before they affect important decisions. Some key responsibilities include:
- Checking outputs: Verify calculations, interpretations, and results before using them.
- Defining constraints: Set clear objectives, permissions, and limits on what an AI agent can do.
- Managing data quality: Ensure that data is relevant, accurate, consistent, and suitable for intended analysis.
- Reviewing decisions: Assess whether recommendations are reasonable and supported by evidence.
- Governance and accountability: Establish clear responsibility for data access, AI use, risk management, and final decisions.
This is where Data Strategy & Governance becomes especially relevant. The MAS in Data Science USA Track includes this area alongside Advanced Machine Learning, Artificial Intelligence Foundations, Big Data Analytics, and Data Engineering & High-Performance Computing. Together, these subjects provide a curriculum-based connection to the technical and governance challenges of increasingly AI-enabled workflows.
Which Parts of a Data Scientist’s Work Could AI Automate?
AI is changing how Data Scientists approach everyday analytical work by assisting with repetitive tasks, routine analysis, and parts of model development. Generative AI and agentic AI can help streamline certain steps, but the extent of automation depends on task complexity, data quality, and the systems being used. Understanding which activities AI can support—and where human expertise remains necessary—is essential for navigating the future of data science careers.
Repetitive Technical Tasks
AI tools can assist Data Scientists with routine coding, data transformation, analytical workflows, and technical documentation. For example, they can suggest Python or SQL code, help identify potential data inconsistencies, generate initial scripts, and prepare draft explanations of an analytical process. These capabilities may reduce manual effort and help professionals iterate more efficiently. However, generated code and transformations still require testing to ensure accuracy, security, and compatibility with existing systems. Data Scientists need to understand the underlying process rather than rely on AI outputs without review.
Routine Data Analysis
AI can support basic exploratory data analysis, identify potential patterns, summarise datasets, and generate initial visualisations or repetitive reports. These capabilities can help professionals examine data and develop hypotheses more quickly. The level of automation depends on the task and data environment. Clean, structured datasets and standardised reporting workflows may be easier to automate than incomplete, inconsistent, or highly specialised data. Data Scientists must still assess whether patterns are meaningful, identify possible bias, distinguish correlation from causation, and determine whether the findings answer the original business question.
Some Parts of Model Development
AI-assisted tools can help Data Scientists explore modelling approaches, generate baseline code, suggest features, and compare model evaluation metrics. These capabilities can support experimentation and reduce some repetitive implementation work. For the future of data science careers, the key shift is not that AI will independently handle every stage of model development. Rather, AI can assist with parts of the workflow while increasing the importance of validation, technical understanding, and sound decision-making. Learning Machine Learning, Artificial Intelligence, and Data Engineering can help professionals understand these workflows and work more effectively with AI-enabled tools.
Which Data Science Skills Are Becoming More Valuable in the AI Era?
With AI tools doing an increasing amount of analysis, Data Scientists will be required to hone their skills to interpret, assess and utilize the results produced through AI. The success of a career in data science is contingent upon the combination of skills in technical competence, quantitative reasoning, reliable data infrastructure and sound decision-making. To those wondering what they should do to prepare for this change, four aspects stand out.
Statistics and Quantitative Reasoning
Statistics and probability help Data Scientists understand uncertainty, evaluate analytical results, and distinguish meaningful patterns from misleading conclusions. These skills are essential when assessing model predictions, interpreting correlations, testing hypotheses, and determining whether findings are supported by sufficient evidence.
Machine Learning and AI
Knowledge about Machine Learning and Artificial Intelligence is becoming important because of the collaboration between Data Scientists and AI-enabled technologies. This knowledge will allow professionals to analyze the output of these AI-based methods rather than just using the output without analysis. The Introduction to Machine Learning, Advanced Machine Learning, and Artificial Intelligence courses in the program provide an educational link to these skills. These courses together enable the understanding of how the models work, how to analyze their performance, and how the AI techniques can be applied to analytical problems.
Data Engineering and Infrastructure
AI systems depend on reliable data foundations, scalable processing, and infrastructure that supports the movement and management of information. Inconsistent data pipelines, poor-quality inputs, and limited computing resources can undermine the performance of even sophisticated models. McKinsey’s 2026 research highlights data readiness and governed data foundations as important considerations for organisations seeking to scale AI. This makes knowledge of data engineering, cloud infrastructure, and high-performance computing relevant to the future of data science careers. The MAS curriculum’s Data Engineering & High-Performance Computing and Big Data Analytics components align with these technical areas.
Data Strategy and Governance
As organisations use AI across more workflows, Data Scientists need to understand how data is collected, managed, accessed, and used responsibly. Data quality, privacy, governance, accountability, and organisational context all influence whether an analytical solution is reliable and appropriate. Governance also helps organisations establish clear responsibilities for reviewing AI-generated outputs, managing risk, and maintaining trustworthy data practices. The Data Strategy & Governance component of the MAS in Data Science curriculum is particularly relevant to this area, connecting technical work with the policies and organisational decisions that guide data use.
Why Human Skills Will Matter More Alongside AI
As artificial intelligence continues to perform routine analyses, judgment by human beings will be critical in deciding how to interpret and act on the data. Artificial intelligence can help to find answers, point out trends, and even come up with solutions to certain problems; however, it is up to Data Scientists to verify whether the output is valid and applicable for the practical purposes of the company. In the future, data science jobs will be not only about skills but also about asking the right questions.
Problem Framing
Before choosing a model or conducting an analysis, it is important for Data Scientists to know what they are solving. It means that the proper questions should be asked, as well as the business objectives should be identified and understood. Moreover, even in cases when AI is capable of suggesting models or creating analyses, the result will strongly depend on how correctly the initial problem is framed.
Critical Thinking and Validation
It is imperative that outputs of AI be seen as something that needs to be evaluated rather than accepted as the answer. In fact, there are certain things that data scientists must do, such as testing any assumption, checking the quality of data, verifying if the conclusions have any statistical and practical significance, among other things. The same applies especially when the analytical output is being used for business decisions.
Communication and Business Understanding
Technical findings create value when decision-makers can understand and act on them. Data Scientists must explain complex results in clear language, connect insights with business objectives, and work with stakeholders to understand their requirements. They also need to communicate uncertainty and limitations so that organisations can make informed decisions rather than relying on numbers without context.
How the MAS in Data Science Curriculum Aligns With the AI-Driven Future
With the increasing use of AI in the processes of Data Science, there is a need for knowledge that goes beyond the fundamentals of data analysis. This requires knowledge of machine learning algorithms, AI technology management, massive data handling, and ensuring that the data-driven system functions using dependable and responsible information. The Master of Applied Science (MAS) in Data Science USA Track program at Illinois Tech, under the edept × Mahindra University × Illinois Tech Global Pathway Program, incorporates coursework on all these aspects.
Machine Learning
Machine Learning remains a core part of modern Data Science because it enables systems to identify patterns, make predictions, and support data-driven decisions. As AI tools automate parts of model development, Data Scientists still need to understand how models work, assess their performance, and determine whether they are suitable for a particular problem. The program’s Introduction to Machine Learning module in the India Track establishes foundational knowledge, while Advanced Machine Learning in the USA Track builds on that foundation. This progression connects introductory concepts with more advanced study, helping students develop a deeper understanding of machine learning techniques and their role in analytical workflows.
Artificial Intelligence
Generative AI and various AI-based technologies have transformed professional data handling practices as well as the interpretation of solutions and outcomes. The comprehension of the theoretical basis is hence essential for the Data Scientist in order to use the technology in an effective manner rather than using the AI tool output without comprehending its restrictions. In the USA Track, Artificial Intelligence Foundations have been included as part of the subject matter in the MAS in Data Science program. Other than machine learning, the subject is instrumental in helping professionals comprehend the technologies that have influenced Data Science.
Big Data and Data Engineering
AI-based analytics rely on having access to data of adequate quality and the necessary technology that will enable efficient handling of this data. With increasing size and complexity of the datasets, companies should have tools that help them collect, manage and prepare data for analytical and machine learning purposes. Otherwise, even advanced models might give inaccurate results and be unable to function properly. In the USAT track, there are classes such as Big Data Analytics and Data Engineering & High Performance Computing, where students learn about the significance of working with large amounts of data and the underlying technology involved. For a future Data Scientist, the above courses can be useful because, along with modelling skills, they provide a more comprehensive approach to working with data.
Data Strategy and Governance
As AI systems increasingly use data to generate recommendations and support decisions, data quality, responsible access, and governance become especially important. Poorly managed data can affect analytical accuracy, while unclear policies around data use can create privacy, compliance, and accountability risks. These concerns become even more significant when AI systems are integrated into automated workflows or are able to take actions based on their outputs. The Data Strategy & Governance module in the USA Track addresses this strategic dimension of Data Science. It complements technical subjects by highlighting the importance of managing data effectively and aligning its use with organisational requirements. For professionals working with AI-enabled systems, governance is not separate from technical work; it helps establish the conditions under which data and AI can be used responsibly.
Will AI Create New Data Science and AI-Related Roles?
As organisations integrate AI into their workflows, opportunities may emerge at the intersection of Data Science, AI development, data infrastructure, and governance. However, it is too early to assume that every emerging specialisation will become a widely established job category. In many cases, existing roles may evolve to include new responsibilities rather than being replaced by entirely new job titles.
AI-Enhanced Data Scientist
An alternate possibility is an AI-empowered Data Scientist who would make greater use of AI technologies. Instead of dedicating as much effort to tedious tasks like coding, preparing data, and conducting analysis, AI would be used to speed up certain steps in their work while allowing them to focus on formulating problems, model assessment, interpretation, and decision-making. An AI-enhanced Data Scientist can be viewed as an evolution of an existing profession, rather than a forecast of the widespread emergence of a new job title. This particular career would involve skills typical for Data Scientists, such as statistics, machine learning, programming, and analytical reasoning, together with evaluation of the outcomes produced by AI.
AI and Data Governance Roles
As AI systems become more involved in analysing information and supporting decisions, organisations need to consider how data is collected, managed, accessed, and used. Data quality, responsible use, and AI oversight can all influence whether these systems produce reliable and appropriate outcomes. This may create opportunities for professionals who combine data expertise with governance, risk management, and responsible AI practices. The Data Strategy & Governance component of the MAS in Data Science USA Track is relevant to this intersection, complementing technical learning with a focus on the strategic management of data. These capabilities can be valuable even when governance responsibilities remain part of an existing role rather than becoming a separate job title.
Data Engineering and AI Infrastructure
AI applications rely on robust data pipeline infrastructures, scalability in computing power, and infrastructure which is capable of handling intensive data tasks. As companies implement more AI-based applications, the processes of preparing data, setting up processing infrastructure, and having reliable infrastructure are still integral to the larger technology environment. Data Engineering & High-Performance Computing skills, along with Big Data Analytics skills, fall under these technical needs. Both fields are offered by the MAS in Data Science USA Track program.
What Will the Data Scientist of the Future Need to Know?
The Data Scientist of the future will need a combination of technical expertise, analytical judgement, and business understanding. As AI tools take on more routine tasks, professionals must be able to interpret results, evaluate model outputs, work with reliable data systems, and translate insights into meaningful decisions. These capabilities bring together traditional Data Science foundations and the skills needed to work effectively in an AI-driven environment.
| Skill Area | Why It Matters in an AI-Driven Environment |
| Statistics & Probability | Evaluate uncertainty and analytical results |
| Machine Learning | Understand and develop predictive systems |
| AI | Work effectively with modern AI technologies |
| Big Data | Analyse data at scale |
| Data Engineering | Build reliable data foundations |
| Governance | Manage data quality, risk and responsible use |
| Business Understanding | Turn analysis into decisions |
| Critical Thinking | Validate AI-generated outputs |
| Communication | Explain insights and recommendations |
The technical skills in this table align with the MAS in Data Science Global Pathway Program curriculum, which includes Python programming, Statistics & Probability for Analytics, Introduction to Machine Learning, Advanced Machine Learning, Artificial Intelligence Foundations, Big Data Analytics, and Data Engineering & High-Performance Computing. Data Visualisation & Business and Data Strategy & Governance further connect technical knowledge with business communication and responsible data management.
Should You Still Pursue a Career in Data Science in 2026?
Following a career in Data Science could prove to be equally viable in 2026 as long as one recognizes the evolution that the field is undergoing. While AI is altering the processes of data preparation, analysis, and use for the development of predictions, it does not remove the necessity of knowledge about the data, its analysis, and problem-solving. The situation regarding the current job market indicates that the future is likely to include the transformation of jobs and skills together with new responsibilities associated with AI. Students who choose their careers have to focus on building a strong foundation in statistics, programming, and analytical thinking while learning how to collaborate with AI. It becomes increasingly clear that the literacy in AI should be seen as complementary to those skills, not as a replacement. Employment chances are going to depend on a number of aspects, including technical skills, experience, business background, and other aspects.
Who May Be Well Positioned?
Students who enjoy quantitative problem-solving and are curious about how data can explain patterns or support decisions may find Data Science worth exploring. An interest in statistics, programming, Machine Learning, and Artificial Intelligence provides a useful starting point, although developing these skills requires consistent practice. No particular background or qualification guarantees employment, but a combination of relevant skills and applied learning can help candidates prepare for opportunities in the field.
What Should Students Learn?
Students should focus on a balanced set of technical and professional skills rather than learning AI tools in isolation. Statistics helps them understand uncertainty, Python supports data manipulation and programming, and Machine Learning provides a foundation for predictive modelling. Knowledge of Artificial Intelligence helps them understand emerging technologies, while Big Data and Data Engineering address the infrastructure needed to process and manage data reliably. The edept × Mahindra University × Illinois Tech MAS in Data Science Global Pathway Program brings together many of these learning areas. Its curriculum includes Programming for Data Science (Python), Statistics & Probability for Analytics, Introduction to Machine Learning, and Data Visualisation & Business in the India Track, followed by Advanced Machine Learning, Artificial Intelligence Foundations, Big Data Analytics, Data Engineering & High-Performance Computing, and Data Strategy & Governance in the USA Track. For students considering a Data Science master’s in the USA, this combination offers a structured way to build knowledge across analytics, AI, data infrastructure, and governance. The broader career lesson is straightforward: rather than avoiding Data Science because of AI, evaluate whether you are willing to develop the technical foundations, practical judgement, and adaptability that the evolving field demands.
How Can a Master’s in Data Science Help You Prepare for AI-Driven Careers?
As AI continues to change how organisations analyse information and develop data-driven solutions, aspiring Data Scientists need to build skills across several connected areas. A structured master’s program can help students develop these capabilities together rather than learning programming, statistics, Machine Learning, and AI as isolated subjects. It can also connect technical knowledge with data infrastructure, governance, and applied project work. The edept × Mahindra University × Illinois Tech MAS in Data Science Global Pathway Program follows this multidisciplinary approach. As outlined in the program brochure, students begin with foundational and applied coursework at Mahindra University before progressing to advanced Data Science coursework at Illinois Tech in Chicago. The curriculum spans programming, statistics, Machine Learning, Artificial Intelligence, Big Data, Data Engineering, and Data Strategy & Governance, alongside project-based learning.
Build AI and Machine Learning Foundations
Understanding AI requires more than knowing how to use the latest tools. Data Scientists need a foundation in how models learn from data, how their performance is evaluated, and where their limitations may affect results. The program introduces students to Introduction to Machine Learning in the India Track and progresses to Advanced Machine Learning in the USA Track. The USA curriculum also includes Artificial Intelligence Foundations, bringing AI concepts into the broader Data Science learning pathway. Together, these subjects provide structured exposure to machine learning methods and AI technologies relevant to evolving analytical workflows.
Build Data and Infrastructure Skills
Applications driven by AI require accurate data, efficient processing mechanisms, and robust infrastructure that can deal with advanced levels of computation. Data Scientists having knowledge of storage, management, preparation, and processing of information are able to evaluate the quality of the data used for analysis and modelling. The India Track comprises Programming for Data Science (Python) and Data Management & SQL, thus laying a foundation for programming and data management. The USA Track covers Big Data Analytics and Data Engineering & High Performance Computing, allowing the scope of studies to widen to the field of big data analytics and computing.
Develop Applied and Strategic Skills
Technical knowledge becomes more useful when students can apply it to practical problems and understand the broader implications of their work. Project-based learning can provide opportunities to connect concepts, work through analytical challenges, and develop the ability to explain findings. Governance knowledge adds another dimension by addressing how data should be managed and used responsibly. The program brochure lists Applied Projects & Assessments in the India Track, followed by a Capstone Project / Master’s Thesis and Industry-Oriented Project Work in the USA Track. The USA curriculum also includes Data Strategy & Governance, which complements technical study with a focus on the strategic management of data.