Summary
Daniel Wells is a statistical genetics leader with 11 years of experience translating large-scale omics data into actionable insights across drug discovery, biomarker development, and clinical risk stratification. He combines a DPhil in functional genomics with hands-on molecular biology experience — including early work on a MERS vaccine that reached phase I trials — to bridge wet-lab intuition and rigorous quantitative modelling. At Genomics he leads teams building and deploying polygenic risk scores and hybrid predictive models, inventing methods (several patents pending/granted) that improve multi-ancestry and rare-variant prediction without reliance on large validation cohorts. He is fluent in Python, C++ and reproducible data pipelines, and routinely communicates technical value to partners and clients while mentoring colleagues in statistics and software engineering. Notably, his doctoral work discovered the meiotic role of ZCWPW1 via single-cell transcriptomics and ChIP-seq, highlighting a rare blend of deep biological discovery and scalable statistical method development. Based in London, he focuses on practical, reproducible solutions that move genetic predictors from research into clinical and commercial use.
11 years of coding experience
6 years of employment as a software developer
Doctor of Philosophy (DPhil) Genomic Medicine and Statistics (functional genomics), Doctor of Philosophy (DPhil) Genomic Medicine and Statistics (functional genomics) at University of Oxford
A Levels, A Levels at Egglescliffe School