I’m finishing my graduate degree in Biostatistics, but my path here started at the lab bench. Over six years working in research labs — with academic roots in Biology and Pharmacy — I learned how biomedical data is actually generated: the protocols, the instruments, the deviations, and the messy realities that never show up in a clean CSV.
That experience shapes how I do statistics. I don’t just fit models; I ask whether the data can support the question, and I build analyses that someone else can reproduce and audit. My day-to-day tools are R (tidyverse, ggplot2) and Python — including a deployed interactive dashboard built with PyShiny — with a focus on survival analysis, mixed models, and clinical trial design.
I’m currently looking for roles in industry data science and clinical-trials biostatistics, where domain knowledge and statistical rigor both matter.
Outside of work, I love to cook and bake — which, like any good experiment, comes down to following a procedure. In this case, the protocol is a recipe.
Skills & Tools
Statistical methods: regression analysis, ANOVA, survival analysis, mixed-effects models, hypothesis testing, study design
Programming: R (tidyverse, ggplot2, Shiny), Python (pandas, PyShiny), Quarto, Git/GitHub
Domain: clinical and regulatory analysis, pharmacy, six years of wet-lab research practice