Practical insights driven by quantitative intelligence
We partner with biotech, pharma, and healthcare leaders to turn complex data into confident decisions. Our work sits at the intersection of statistical rigor and practical judgment — because in high-stakes environments, the right analysis isn't just technical. It's ethical.
Phronesis (φρόνησις) — practical wisdom — is the virtue of deliberating well about what is good and beneficial for oneself and others, not in the abstract, but in the particular circumstances of action. It is not mere technical skill nor theoretical knowledge, but the capacity to apply knowledge, experience, and good judgment to novel, high-stakes situations where the "right answer" isn't in a textbook.
— Aristotle, Nicomachean Ethics, Book VI
In biotech R&D and clinical decision-making, the data is never complete, the models are always imperfect, and the cost of error is measured in patient outcomes and wasted capital. Good decision-making is an ethical imperative. We bring quantitative intelligence — causal inference, robust study design, Bayesian reasoning, sensitivity analysis — not to replace judgment, but to inform it. The goal is not certainty. It's defensible, transparent, resilient reasoning that holds up when the tide turns.
Quantitative researcher and developer with publications in computer vision, device & platform development, and algorithmic decision making. A former principal data scientist at a clinical-stage biotech who built reproducible analysis pipelines used by researchers across multiple therapeutic areas. The PhD isn't a credential — it's a commitment to the standards of evidence that this work demands.
Resilient insights for shifting landscapes. Regulatory frameworks evolve. Clinical paradigms shift. Data sources fragment. We help you develop insights that last and degrade gracefully — explicit assumptions, sensitivity bounds, reproducible pipelines — so your decisions remain defensible when the ground moves.
How we deliver quantitative intelligence that matters
We choose the right tool for the question — not the other way around. Frequentist, Bayesian, causal, simulation-based — whatever yields the most transparent, defensible answer.
Every analysis begins by making assumptions explicit. We document, test, and bound them — so you know exactly what your conclusions depend on.
Versioned code, pinned environments, documented data lineage. Every deliverable ships with the computational recipe to regenerate it.
Analysis serves decisions. We frame outputs around action thresholds, value of information, and the cost of being wrong — not p-values alone.
In healthcare and biotech, analytical choices affect patients. We treat methodological transparency as a moral obligation, not a nice-to-have.
We embed with your team — not as a black box, but as collaborators who translate between technical and clinical languages.