Data Science · Biostatistics · R & Python
Biostatistician & data scientist
My work spans clinical trials, real-world evidence, claims, observational studies, and biomedical data. I lead analysis projects end to end: defining the question, carrying out the statistical work, building supporting software, and communicating the results.
What I do
Healthcare data analysis
My work focuses on turning complex healthcare data into useful evidence. That includes choosing appropriate statistical methods, building reproducible analysis workflows, and developing software that makes results easier to explore and communicate.
01
Healthcare data
Clinical trials, real-world evidence, claims, observational cohorts, patient-reported outcomes, and biomarker data.
02
Statistics and machine learning
Survival and longitudinal analyses, causal inference, mixed-effects models, simulation, predictive modeling, and subgroup analyses.
03
Analytical software
Reproducible workflows, R packages, Shiny applications, dashboards, testing, and deployment using R, Python, and related tools.
Selected projects
Public software
R package · v0.2.0
shinydataviewer
A reusable Shiny module for viewing tabular data with a searchable table and variable-summary sidebar.
Shiny application · Public dashboard
Massachusetts Water Quality
An interactive dashboard for exploring Massachusetts water-quality data by municipality and chemical. An update using newer or streaming data is planned.
Background
Experience across healthcare data
My experience spans biopharma, contract research, and real-world data. Across these settings, I have worked cross-functionally with clinical, translational, medical affairs, biostatistics, data science, data engineering, IT, and operational teams.
In senior roles, I have led technical work, helped set standards for code quality and validation, and mentored colleagues. I care about clear communication, reproducible analysis, and tools that other people can maintain.