I’m a data scientist with a research background in causal inference and experimental design. At the University of Oxford, I designed and executed an A/B test end-to-end, from questionnaire design and sample recruitment to analysis, studying financial product adoption, then built a Causal Forest model that improved user targeting efficiency by 23%. At the European Commission, I designed ETL pipelines processing millions of records across 12 countries to support evidence-based policy.
My work sits at the intersection of rigorous statistical thinking and practical impact: designing experiments, building predictive models, and turning messy data into decisions that hold up under scrutiny. I hold a PhD in Quantitative Social Sciences from the University of Lausanne and a Master’s in Data Science from the University of Texas at Austin. I’m most at home working with Python, R, and SQL on problems where getting the causal story right actually matters.
I’m based in Toronto and open to new opportunities. Take a look around, or get in touch on: moawad.jad@gmail.com
