Issue 5: Data Scientist
AI job disruption forecasting — Issue 5
As artificial intelligence continues to evolve, the role of Data Scientists is poised for significant change.
Figure 1: Automation Potential
Figure 1 highlights the automation potential and projected timelines for the core tasks of a Data Scientist. Data-heavy activities such as “Process digital or online data” and “Provide information to guests or customers” exhibit high automation potential (90–95%), with disruption likely between 2025 and 2029. On the other hand, more strategic and creative tasks like “Develop scientific or mathematical theories” and “Build structures” show lower susceptibility (5–50%) and a longer timeline extending into the 2030s.
Figure 2: AGI Disruption
Figure 2 explores an Artificial General Intelligence (AGI) scenario, where automation potential surges across all activities. In this future, even complex and cognitive tasks such as “Analyze business or financial data” and “Develop models of systems or processes” could experience automation levels of 90–95%. This suggests that as AI systems approach general intelligence, they may be able to replicate much of the analytical and problem-solving work traditionally done by Data Scientists.
It is therefore important to future-proof data science careers by focusing on human-centric skills such as domain expertise, stakeholder communication, and ethical oversight—areas where AI is still far from matching human judgment.