Samuel Alarcon
Quantitative Research
The through-line in Samuel’s career is the relationship between economic theory and real decisions. He began as a researcher and graduate-level professor — teaching Macroeconometrics and Microeconometrics at master’s level, publishing peer-reviewed work in the Latin-American Journal of Economic Development, and contributing econometric analysis to national economic policy at Bolivia’s Ministry of Economy.
That foundation translated directly into applied data science. Samuel has delivered quantitative solutions to McKinsey & Company, Georgetown University, the University of Wales, and the University of Göttingen — working across econometric modeling, time series forecasting, portfolio optimization, and the causal inference work that financial institutions and governments actually act on.
His technical focus within the collective sits at the intersection of statistical economics and machine learning — particularly quantitative finance (portfolio analytics, risk-adjusted return modeling, annuities and structured financial products), macroeconomic forecasting, and DSGE and stochastic modeling that grounds prediction in structural theory rather than pattern-matching alone.
Best Research Paper, Central Bank of Bolivia · Banco Santander scholarship for graduate studies · Published in two peer-reviewed journals.
Master’s in Economic Analysis, Universidad Carlos III de Madrid · Master’s in Economics, ILADES-Georgetown University