I build end-to-end analytical work that turns messy problems into decisions.

Research, Causal Inference, and Applied ML

• Design and run experiments: randomization plans, incentive design, survey/behavioral measurement, and power analysis (sample size planning)

• Build credible causal estimates: difference-in-differences/event studies, fixed effects, instrumental variables, robustness and sensitivity checks, heterogeneous effects

• Use machine learning when it adds value: prediction and segmentation with cross-validation, model selection, and interpretable outputs

Data and Analytics

• Clean, merge, and validate data; define analysis-ready datasets with clear documentation

• Write efficient queries and build reproducible analysis workflows for ongoing reporting and one-off deep dives

Evaluation and Decision Support

• Define outcomes and KPIs, translate goals into measurable metrics, and evaluate programs/products/policies

• Run scenario and cost-effectiveness analysis; map assumptions and quantify uncertainty to support decisions

Communication and Execution

• Produce executive-ready synthesis: concise memos, clear visuals, and decision-focused recommendations

• Scope ambiguous problems, align stakeholders, and own analytical workstreams end-to-end

Tools

• Python, R, Stata, SQL; Git; reproducible reporting (notebooks, Quarto/Markdown)