How Agentic AI is Redefining Data Science Careers

As the end of 2024 approaches, industries are beginning to shift their focus from conversations about generative AI and LLMs to building agentic AI frameworks for their businesses. People even debate whether a single founder with a bunch of AI agents can run a company. This has also raised the question of the relevance of data scientists.

While talking to AIM, Indrajit Mitradirector of data science at Tredence, emphasized the fact that agent AI will drastically disrupt industries and create great value. But far from making data scientists obsolete, it will reshape their roles, skills and responsibilities.

Agentic AI requires a shift in mindset and skills. Traditionally, data scientists focus on predefined problems – extracting insights and building models within clear problem frameworks. However, Indrajit noted that agent AI will require data scientists to proactively tackle complex problems and explore innovative solutions.

“The most important change is that data scientists will need to hit problems, not just solve them. They must first see themselves as business agents and understand the critical challenges facing businesses,” said Indrajit.

Upskilling in the AI ​​era

To excel in this era, data scientists must develop a deeper understanding of business nuances and technical environments. While fundamental knowledge in statistics, machine learning and deep learning will remain essential, the focus will shift towards reinforcement learning, unsupervised learning and deep AI frameworks.

“Data scientists need to reorient their technical skills and in turn upgrade their skills. They need to develop expertise in agentic AI frameworks and platforms while also mastering systems that integrate business insights and technical capabilities,” added Indrajit.

Additionally, data scientists will no longer operate in silos. A strong understanding of broader ecosystems—cloud computing, DevOps practices, and API integrations—will be critical. The ability to fine-tune performance across multiple data sources and domains will be critical to delivering efficient and autonomous systems.

Data Scientists as Orchestrators in an Agentic AI World

In a world where agent AI promises autonomous decision-making, many wonder if these systems can function without data scientists. Indrajit is convinced that they cannot. While agentic AI can operate autonomously in specific contexts, data scientists remain central to designing, implementing, and optimizing these systems.

“Agent AI cannot survive without data scientists. They are needed to design the solutions, train models, integrate systems and continuously monitor performance to align with business expectations,” explained Indrajit.

He used the analogy of a conductor in an orchestra to describe the evolving role of data scientists. Like conductors who understand the audience, the instruments and the musicians, data scientists will orchestrate agentic AI systems to align business goals with technical execution.

“Data scientists will play the role of a master coordinator – connecting between AI platform specialists, agentic AI frameworks and business stakeholders. Their success will depend on balancing these elements while ensuring seamless integration and efficiency,” elaborated Indrajit.

Ethics, Governance and AI Engineering

With the advent of agent AI, ethical considerations, governance and responsible AI engineering become even more critical. While these trends have already begun in industries such as healthcare, finance, and autonomous vehicles, their importance will only grow in the agentic AI era.

Indrajit pointed out how AI is transforming industries such as healthcare, where AI-based diagnosis and patient management raise concerns about privacy, bias and transparency. Financial institutions are also incorporating AI governance to comply with ethical and regulatory standards, such as the EU AI Act and the Dodd-Frank Act.

“Organizations are hiring data scientists with expertise in AI ethics to ensure responsible development of AI models. Data scientists will need to work with ethicists, regulators and legal experts to ensure that agentic AI systems are transparent, accountable and aligned with societal values,” Indrajit pointed out.

The role of data scientists in multimodal AI

While agent AI is one shift, the ever-growing acceptance of multimodal AI presents another level of challenge. Multimodal AI takes different data inputs from a computer, such as text, images, and audio, and generates insights independently. This has sparked the notion that data scientists may be losing control of these systems.

Dismissing this notion, Indrajit emphasized that data scientists are best placed to overcome the challenges posed by multimodal AI. Their expertise is essential to ensure data transparency, provenance and interpretation.

“Data scientists are critical to interpreting multimodal AI output and securing insights. They validate data authenticity, trace inputs back to source data, and continuously audit data. Techniques such as attention mechanisms and saliency maps require human oversight, and data scientists are best suited for these tasks,” Indrajit further said.

The data scientist in the loop

The emergence of agent AI and multimodal systems marks a transformative phase for data science. Far from replacing data scientists, these advances will elevate their roles and place them at the intersection of business strategy, technical innovation and ethical governance.

“Data scientists will play a central role in translating the potential of agent AI into real business value. They will act as orchestrators, balancing technical frameworks, business goals and ethical considerations,” concluded Indrajit.

In this evolving landscape, data scientists must embrace new skills, deepen their domain expertise, and position themselves as indispensable leaders in an AI-driven future. By doing so, they will ensure that agentic AI systems are not only effective, but also aligned with business and societal needs.