Duncan Garmonsway is a data scientist with 13 years’ experience applying statistical rigor and pragmatic engineering to public-sector and large-scale operational problems. He combines graduate training in mathematics and statistics with strong programming and database skills, producing reproducible R workflows and authoring the tidyxl and unpivotr packages. His work at Government Digital Service spans probabilistic record linkage, graph-based fraud analysis, interpretable clickstream embeddings and temporal topic modelling, reflecting a taste for both methodological innovation and production-ready delivery. Previous roles include building interim reporting infrastructure for a national smart-meter rollout, pioneering larger-than-memory R processing, and automating time-series reporting for defence workforce planning. A clear communicator and mentor, he routinely translates complex data narratives for stakeholders and supervises aspiring data scientists. Colleagues describe him as versatile and fast to acquire domain expertise, with a knack for uncovering pragmatic fixes that deliver measurable operational savings.
12 years of coding experience
1 year of employment as a software developer
Graduate Diploma in Science (GDipSci), Mathematics and Statistics, Graduate Diploma in Science (GDipSci), Mathematics and Statistics at Victoria University of Wellington
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