Jack M.

I help teams answer “what works?” with clear, decision-ready analysis — especially when the data is messy, multi-source, and high-stakes. My strengths are causal inference and evaluation (DiD/event studies, quasi-experiments), paired with strong Python/SQL execution: building tidy, well-documented datasets, running robust analyses, and communicating results in a way stakeholders can act on. Typical projects I do well: - Program / policy / product impact evaluation (design, analysis, robustness, write-up) - Metric definitions, KPI tracking, and executive-ready insights - Reproducible data workflows: entity linkage, QA checks, feature tables, and handoff docs What you can expect working with me: - Structured approach (scope → data audit → analysis plan → delivery) - Transparent assumptions and sanity checks - Clean, reproducible code + concise documentation