How Agentic AI is redefining careers in data science

As the end of 2024 approaches, industries have begun to shift their focus from conversations about generative AI and LLMs to building agentic AI frameworks for their companies. There is even debate about whether a single founder can run a company with a group of AI agents. This has also raised questions about the relevance of data scientists.

When talking to GOALIndrajit Mitra, Director of Data Science at Tredence, highlighted the fact that agentic AI will drastically transform the industry and create great value. However, it will not make data scientists obsolete, but rather 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 pointed out that agent AI requires data scientists to proactively formulate complex problems and explore innovative solutions.

“The key change is that data scientists need to formulate problems, not just solve them. They must first look at themselves as agents of the business and understand the critical challenges that businesses face,” Indrajit explained.

Further training in the age of AI

To be successful during this time, data scientists must develop a deeper understanding of business nuances and technical environments. While basic knowledge of statistics, machine learning and deep learning remain essential, the focus will shift to reinforcement learning, unsupervised learning and deep AI frameworks.

“Data scientists need to refocus their technical skills and thereby upskill themselves. They must develop expertise in agent AI frameworks and platforms while mastering systems that integrate business insights and technical capabilities,” Indrajit added.

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

Data scientists as orchestrators in an agent AI world

In a world where agentic AI promises autonomous decision-making, many wonder whether these systems can function without data scientists. Indrajit strongly believes that this is not possible. While agentic AI can function autonomously in certain contexts, data scientists continue to play a central role in the design, deployment and optimization of 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,” Indrajit explained.

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 agent AI systems to balance business goals with technical implementation.

“Data scientists will play the role of a lead coordinator – the interface between AI platform specialists, agent AI frameworks and business stakeholders. Their success will depend on the balance of these elements while ensuring seamless integration and efficiency,” Indrajit explained.

Ethics, governance and AI engineering

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

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 integrating 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 must work with ethicists, regulators and legal experts to ensure that agent AI systems are transparent, accountable and consistent with societal values,” Indrajit emphasized.

The role of data scientists in multimodal AI

While agent AI is a game changer, the ever-growing adoption of multimodal AI poses another challenge. Multimodal AI takes various data inputs from a computer, such as text, images and audio, and independently generates insights. This has led to the belief that data scientists may lose control of these systems.

Indrajit rejected this notion, emphasizing that data scientists are best positioned to address the challenges of multimodal AI. Their expertise is critical to ensuring data transparency, provenance and interpretability.

“Data scientists are critical to interpreting multimodal AI results and securing insights. They validate data authenticity, trace inputs back to the source data, and continually review data. Techniques like attention mechanisms and saliency maps require human supervision, and data scientists are best suited for these tasks,” Indrajit further said.

The data scientist in the loop

The emergence of agentic AI and multimodal systems marks a transformative phase for data science. These advances will not replace data scientists, but will strengthen their role and bring them to the intersection of business strategy, technical innovation and ethical governance.

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

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