Datageek Designs is an independent practice specializing in health data science and quantitative research infrastructure. Engagements draw on graduate research in computational causal inference at Dartmouth and prior industry experience in FDA-regulated biopharmaceutical manufacturing at Bristol Myers Squibb and Moderna.
Population health surveillance from complex survey data, including NHANES, BRFSS, and related federal health datasets. Work covers survey-weight-aware estimation, standardization, and descriptive analysis systems built for reproducibility and presentation to non-technical stakeholders.
Causal analysis for health policy and outcomes research using methods including difference-in-differences, interrupted time series, event study designs, and propensity-based estimators. Deliverables are documented pipelines suited for peer review, regulatory reporting, or internal decision-support.
Backend systems for research and health analytics: relational database design, data ingestion APIs, and ETL pipelines oriented toward reproducibility, auditability, and long-term maintainability in regulated or academic environments.
Bespoke analysis tools and interactive reporting interfaces. Prior work includes DAG-based causal modeling software, simulation libraries, and browser-based health dashboards built for researchers who need interpretable, shareable output.
Open to engagements in health data science, causal analysis, and research software. Project-based and retainer arrangements available.
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