Giancarlo Salazar-Caicedo is an applied research scholar, econometrician, and the Principal of Econometricus LLC, an analytics firm specializing in advanced econometric modeling. His academic background spans multiple fields; he earned a Master of Science from the University of Massachusetts Boston—where he researched macroeconomic trickle-down effects—and a degree in History from the Pontificia Universidad Javeriana in Colombia. Salazar-Caicedo’s expert research integrates econometrics, open-source AI, and natural language processing. He frequently investigates macroeconomic policy, central bank communications, and complex data-matching algorithms.

Carlos Lara

Senior Quantitative Economist & Econometrician

Dr. Carlos Lara is an applied econometrician specializing in causal inference, high-dimensional data analysis, and the intersection of machine learning with traditional econometrics. He holds a PhD in Economics, where his doctoral research focused on developing microeconometric frameworks to evaluate the long-term labor market impacts of automation and targeted public policy interventions.

Currently, Dr. Lara serves as a Senior Quantitative Economist, leading a team of data scientists and researchers in designing structural models and predictive algorithms for complex economic environments. His expertise includes:

• Advanced Causal Inference: Designing quasi-experimental methods, difference-in-differences frameworks, and synthetic control models for policy evaluation.
• Predictive Analytics: Integrating open-source Python and R machine learning pipelines with traditional time-series and panel data models.
• Large-Scale Data Architecture: Processing and analyzing massive, unstructured administrative datasets to extract actionable economic insights.

Dr. Lara is a frequent speaker at international economic conferences and actively contributes to peer-reviewed journals. He is passionate about bridging the gap between rigorous academic theory and scalable, data-driven business decisions.

Patricia De Las Salas

Senior Quantitative Policy Analyst & Lead Statistician

Dr. Patricia De Las Salas is an expert statistician specializing in public policy design, evidence-based program evaluation, and data-driven governance. She holds a Doctorate in Statistics from the University of Buenos Aires (UBA), where her doctoral research focused on developing robust non-parametric statistical models to measure the socioeconomic outcomes of regional development programs.

Currently, Dr. De Las Salas works as a senior consultant and analyst, partnering with government agencies, international NGOs, and think tanks to measure the effectiveness of public interventions. Her methodology combines rigorous mathematical foundations with practical, scalable applications to solve complex societal challenges.

Areas of Expertise

• Program Evaluation: Designing randomized controlled trials (RCTs) and quasi-experimental frameworks to measure policy impact.
• Socioeconomic Modeling: Utilizing advanced survey sampling, multivariate analysis, and longitudinal data modeling to track demographic trends.
• Data-Driven Governance: Translating complex statistical findings into actionable policy recommendations for executive decision-makers.
• Advanced Analytics: Developing open-source statistical pipelines in R and Python for large-scale public administration datasets.

Dr. De Las Salas is dedicated to advancing public sector transparency and efficiency through rigorous quantitative proof. She regularly speaks at international development conferences and conducts workshops on modern statistical methods for public policy professionals.