Statistical design
Sharper studies before expensive work begins
Endpoint strategy, model planning, cohort definitions, missing-data handling, and study design guidance.
Quantitative strategy for biotech, medtech, diagnostics, and research teams
Bayesian Science partners with life science companies to build credible statistical workflows, defensible models, and decision-ready evidence across R&D, translational work, clinical programs, and product strategy.
Bayesian inference
Clinical analytics
Biomarker data
Reproducible pipelines
Scientific dashboards
What we do
Statistical design
Endpoint strategy, model planning, cohort definitions, missing-data handling, and study design guidance.
Data engineering
Harmonization, quality control, assay integration, and reproducible analysis layers for growing teams.
Scientific communication
Figures, memos, technical narratives, and executive summaries that make complex findings easier to act on.
How we work
We start with the scientific or operational decision, not just the model request.
We audit structure, provenance, missingness, and variability before drawing conclusions.
We choose methods that reflect the biology, the uncertainty, and the real decision context.
Stakeholder-ready deliverables make findings legible to scientists, operators, executives, and investors.
Where we add value
Clarify endpoints, subgroup behavior, biomarker signals, and risk before major decisions are made.
Improve validation logic, performance analyses, and reporting confidence for technical and commercial teams.
Modernize scientific workflows so analyses are reproducible, inspectable, and faster to iterate on.
Ready to talk