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Reetik is a research analyst leading the development of the MACROCOSM Canada model, an agent-based macroeconomic forecasting framework. His expertise lies in optimization, decision-making under uncertainty, and machine learning, with applications in natural resources and sustainability challenges. Prior to joining SESIT, he was a research scholar at IIASA, where he contributed to hydro-economic modeling, climate risk assessment, and policy analysis for organizations such as the World Bank and Austrian Environmental Agency.

Reetik received his Ph.D. in Computational Science for Resource Engineering from MIT, where his research focused on multi-agent decision frameworks for water resource management, incorporating stochastic optimization and game theory. He has applied these methods to analyze common-pool resource problems, hydroeconomic trade-offs, and adaptation strategies under climate change. His postdoctoral work at Berkeley National Lab involved developing machine-learning-driven decision support systems for sustainable groundwater management.