Connor Mcisaac is a Lead Machine Learning Engineer with eight years’ experience applying data processing, statistics and ML to problems ranging from commercial analytics to gravitational wave astronomy. After a PhD in gravitational waves and research stints with ESA and academic collaborations, he transitioned to industry roles where he now develops and deploys ML models across Virgin Money’s Advanced Analytics. He has practical expertise in PySpark, SQL, NLP and causal analysis, and a track record of working effectively in both small teams and large scientific collaborations. Connor excels at communicating complex results to technical and non-technical audiences and enjoys diving deep into code or designing compelling data visualisations. He brings a researcher’s rigor to production ML, pairing curiosity about fundamental science with a focus on delivering business impact. Based in Larbert, Scotland, he is motivated by solving unconventional problems and translating complex datasets into actionable insight.
8 years of coding experience
3 years of employment as a software developer
Doctor of Philosophy - PhD, Gravitational Waves, Doctor of Philosophy - PhD, Gravitational Waves at University of Portsmouth
Master's degree, Physics with Astrophysics, First, Master's degree, Physics with Astrophysics, First at The University of Glasgow
Core package to analyze gravitational-wave data, find signals, and study their parameters. This package was used in the first direct detection of gravitational waves (GW150914), and is used in the ongoing analysis of LIGO/Virgo data.
Contributions:21 pushes, 27 branches in 3 years 8 months
Contributions:11 commits, 10 pushes, 1 branch in 8 months
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Connor Mcisaac - Lead Machine Learning Engineer at Virgin Money