Data Scientist & Development Economics Researcher
Working at the intersection of causal inference and evidence-based policy, focused on poverty reduction through global health solutions and applying AI for social good.
I work at the intersection of global health, AI safety, and data-driven development, applying quantitative research and machine learning to address poverty, reduce suffering, and improve decision-making in climate change, education, and agriculture. My focus is on building scalable, cost-effective solutions that translate rigorous analysis into real-world impact.
My methodological orientation centres on the credible identification of causal effects through randomized controlled trials (RCTs), quasi-experimental methods, and the careful application of econometric theory to field data. I place strong emphasis on measurement validity and decision-making under uncertainty, ensuring that evidence is robust, policy-relevant, and embedded in programme design from the outset. I work extensively with R and Python, and apply machine learning tools alongside advanced statistical techniques to generate actionable insights.
Across field and research roles, I have managed data systems, designed measurement frameworks under operational constraints, and conducted quantitative analysis to inform adaptive management, funding allocation, and strategic planning. My work prioritizes cost-effectiveness analysis and evidence-based resource allocation.
I am also the founder of EA Somalia, building a community committed to effective altruism principles and evidence-based action.
A six-course graduate-level certificate programme integrating microeconomic theory, statistical inference, experimental design, and development policy. Designed for practitioners and researchers working at the frontier of evidence-based development. Includes coursework in Data Analysis for Social Scientists, Designing and Running Randomized Evaluations, and Advanced Econometrics.
Foundation in economic theory, quantitative methods, and analytical reasoning, applied to development and public policy contexts.
Developing an auditable, metrics-based scoring system for responsible AI market conduct. The project synthesises policy and academic literature on market-shaping instruments, translates qualitative governance frameworks into quantitative metrics, and operationalises them into a reproducible evaluation tool.
This study proposes a quantitative framework to assess drought vulnerability in Somalia. Using a dataset from FAO-SWIMS, the paper develops a composite index designed to support evidence-based decision-making for drought preparedness and response planning. The framework aims to strengthen early warning systems, improve resource targeting, and enhance strategic planning for anticipated drought events.
Open to research collaborations, M&E consulting, and academic discussion in development economics, causal inference, and AI safety. Also actively exploring PhD opportunities.