Driven by a commitment to evidence-based decision-making, I leverage quantitative methods to support timely and effective humanitarian responses. With over five years of professional experience in UN field operations, a foundation in liberal arts, a Spatial Data Science certification from Esri, and ongoing graduate studies in Applied Statistics and Data Science, I excel at translating complex data into actionable insights for crisis-affected communities. 

Key Experience 

• United Nations (OCHA & UNDP) 

• Data Collection & Management
  • Designed and optimized end-to-end ETL pipelines in R and Python to ingest, clean, and harmonize data from Multi-Cluster Initial Rapid Assessments (MIRA), the Emergency Response Tracker (ERT), and the Inter-Agency Group on Mixed Migration Flows (GIFMM).
  • Integrated and synchronized information across platforms such as ActivityInfo and HNO/HNP databases, reducing reporting latency by up to 40%. 

• Monitoring & Evaluation Frameworks
  • Co-developed M&E dashboards in Power BI and Shiny to track key performance indicators (KPIs) across multiple clusters (health, WASH, protection).
  • Established metadata standards and automated data-quality checks, ensuring consistency and comparability of indicators over successive monitoring rounds. 

• Predictive Analytics & Early Warning
  • Built regression and machine-learning models to forecast displacement flows under La Niña and volcanic risk scenarios, informing pre-positioning of relief supplies and contingency planning.
  • Developed vulnerability indices by combining demographic, nutritional, and infrastructure data, enabling prioritization of the most at-risk populations. 

• Capacity Building & Collaboration
  • Led workshops for national and sub-national partner agencies on best practices in data cleaning, statistical analysis, and visualization.
  • Mentored junior analysts and interns, fostering a culture of reproducibility through version-controlled scripts and shared code repositories. 

Technical Skills
R (dplyr, tidyr, FactoMineR, pROC), Python (pandas, scikit-learn, TensorFlow), Power BI, Shiny, SQL, DAX, agile project management, statistical modeling, and data governance. 

My curiosity fuels continuous learning: I integrate advances in AI, advanced analytics, and statistical techniques to design more resilient, transparent, and sustainable humanitarian interventions.