James Bristow
Data Scientist
Auckland, Auckland, New Zealand
Summary
👤
Senior🎓
Top SchoolJames Bristow is a data scientist based in Auckland with nine years’ experience applying Bayesian machine learning to marine conservation, fisheries science, and statistical genetics. He combines advanced probabilistic methods—Gaussian processes, deep kernel learning, normalising flows and ABC—with practical HPC deployment (Docker/Apptainer, Kubernetes, SLURM) to turn computationally intensive simulations into production-ready models. His work spans spatiotemporal climate-change modelling, surrogate models for Bayesian optimisation, and point-cloud/network analysis, reflecting a rare blend of statistical rigour and systems engineering. Currently at the New Zealand Institute for Bioeconomy Science, he completed a PhD in Statistics and has a track record of embedding research code into scalable pipelines and AutoML flows. Notably, he moves fluidly between research-grade simulation inference and production tooling, making complex Bayesian workflows reproducible and runnable on supercomputers.
9 years of coding experience
5 years of employment as a software developer
Doctor of Philosophy - PhD, Statistics, Doctor of Philosophy - PhD, Statistics at Massey University
English
Github Skills (158)
abc10
tidyverse10
number-recognition10
sentiment-classification10
anomaly-detection-algorithm10
r9
statistics9
textual9
sentiment-analysis9
stan9
anomaly-detection9
python9
svm9
clustering9
drupal-module9
Programming languages (12)
Github contributions (5)
JBris/dolphin_segmentation
May 2021 - May 2021
Contributions:69 commits, 66 pushes, 1 branch in 19 days
JBris/linux-package-search
Feb 2020 - Mar 2020
A simple tool to search for Linux package information from various distros. Includes Node, Knex, and Swagger. Supports archiving information into Postgres, indexing information using Elasticsearch, and caching search results using Redis.
Contributions:40 commits, 37 pushes, 1 branch in 9 days
cachingelasticsearchlinuxpostgresqlredis
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