We're statisticians, DevOps engineers, and software engineers with strong math and technical backgrounds. This combination lets us approach problems from both the analytical and engineering sides.
Our work includes clinical data analysis, real-world data (RWD) studies, network meta-analysis, and database design. We build data pipelines, design databases, and create analytical infrastructure for organizations that need statistical rigor and technical reliability.
AI tools are powerful, but they need human judgment. Someone has to ask the right questions, choose appropriate methods, and verify the results make sense. We handle that layer—the statistical thinking and engineering decisions that determine whether automated analysis actually works.
We're curious people who like finding patterns in data. Whether it's clinical trial results or cycling metrics, if there's a story in the numbers, we want to know it. When we're not working, we're usually outside—hiking, cycling, skiing. We're building tools to analyze fitness data and generate personalized insights based on individual performance patterns. The same analytical approach we use for client work applies to everything else.