How Agentic AI is Redefining Data Science Careers
As the end of 2024 approaches, industries have started to shift their focus from conversations about generative AI and LLMs to building agentic AI frameworks for their companies. People are even discussing 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 speaking with AIM, Indrajit Mitra, director of data science at Tredence, underscored the fact that agentic AI will drastically disrupt industries and create great value. However, far from rendering data scientists obsolete, it will reshape their roles, skills, and responsibilities. Agentic AI demands a shift in mindset and skills. Traditionally, data scientists focus on predefined problems – extracting insights and building models within clear problem frames. However, Indrajit noted that agentic AI will require data scientists to proactively frame complex problems and explore innovative solutions. “The key change is that data scientists will need to frame problems, not just solve them. They need to view themselves as agents of business first and understand the critical challenges companies face,” Indrajit stated. Upskilling in the Age of AI To excel in this era, data scientists must develop a deeper understanding of business nuances and technical environments. While foundational knowledge in statistics, machine learning, and deep learning remains 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 upskill. They must develop expertise in agentic AI frameworks and platforms while also mastering systems that integrate business insights and technical capabilities,” Indrajit added. Additionally, data scientists will no longer operate in silos. A strong grasp of broader ecosystems – cloud computing, DevOps practices, and API integrations – will become critical. The ability to fine-tune performance across multiple data sources and domains will be essential to delivering efficient and autonomous systems. Data Scientists as Orchestrators in an Agentic AI World In a world where agentic AI promises autonomous decision-making, many wonder whether these systems can operate without data scientists. Indrajit firmly believes they cannot. While agentic AI can function autonomously in specific contexts, data scientists remain central to designing, deploying, and optimising these systems. “Agentic 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 agentic AI systems to align business goals with technical execution. “Data scientists will play the role of a master coordinator – interlocking between AI platform specialists, agentic AI frameworks, and business stakeholders. Their success will depend on balancing these elements while ensuring seamless integration and efficiency,” Indrajit elaborated. Ethics, Governance, and AI Engineering With the rise of agentic AI, ethical considerations, governance, and responsible AI engineering are becoming 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 over privacy, bias, and transparency. Financial institutions are also incorporating AI governance to adhere to ethical and regulatory standards, such as the EU AI Act and the Dodd-Frank Act. “Organisations are hiring data scientists with expertise in AI ethics to ensure responsible development of AI models. Data scientists will need to work alongside 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 agentic AI is one shift, the ever-growing acceptance of multimodal AI poses 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 triggered the notion that data scientists might be losing control of these systems. Repudiating this notion, Indrajit stressed that data scientists are best placed to overcome the challenges posed by multimodal AI. Their expertise is essential for ensuring data transparency, provenance, and interpretability. “Data scientists are critical for interpreting multimodal AI outputs and safeguarding insights. They validate data authenticity, trace inputs back to source data, and audit data continuously. Techniques like 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 advent of agentic AI and multimodal systems marks a transformative phase for data science. Far from replacing data scientists, these advancements will elevate their roles and place them at the intersection of business strategy, technical innovation, and ethical governance. “Data scientists will play a pivotal role in translating agentic AI’s potential into real business value. They will act as orchestrators, balancing technical frameworks, business goals, and ethical considerations,” Indrajit concluded. 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.
To excel in this era, data scientists must develop a deeper understanding of business nuances and technical environments.
To excel in this era, data scientists must develop a deeper understanding of business nuances and technical environments.
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Mohit writes about AI in simple, explainable, and often funny words. He's especially passionate about chatting with those building AI for Bharat, with the occasional detour into AGI.