An interpretable system that models the future of work as an equilibrium under AI-driven forces. Instead of predicting job loss, it decomposes workforce disruption into automation pressure, adaptability, skill transferability, demand, and AI augmentation to explain stability, tension, and transition paths by 2030.

Power BI dashboard analyzing AI's impact on job roles by 2030, including automation risk, salary trends, AI exposure, and technology growth factors.

Analysis of the 2025 Stack Overflow Survey exploring AI's impact on Data Science jobs. Built with Python, Pandas, Seaborn, and Scikit-Learn.

Reproducible Bayesian model of how AI capability progression projects onto global labour displacement. Sigmoid-fitted SWE-bench, vectorised Monte Carlo, hierarchical PyMC sector model, temporal CV.

Know exactly which skills to invest in to become irreplaceable in the age of AI. 88 roles, 1,028 skills, each rated for AI impact.

A Streamlit dashboard analyzing the impact of AI announcements and model releases on equity prices and derivatives markets. Features event study methodology (CAR/CAAR), 3D implied volatility surfaces, real-time sentiment analysis with VADER, and interactive portfolio simulation.

AI exposure index for the European workforce built from Eurostat employment data, ESCO skills taxonomy, and LLM task scoring.

🤖 Analyze the future of work with the Workforce Disruption Equilibrium Engine, a system for understanding job changes in an AI-driven world.