Luke Lewis-borrell - Data Scientist

Luke Lewis-borrell

Data Scientist

Halifax, Nova Scotia, Canada

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Summary

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Rockstar

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Top School

Luke Lewis-borrell is a data scientist with six years of experience who transitioned from a PhD in physical chemistry to building scalable data and ML systems for industry and public health. He has designed cloud-native ingestion and analytics architectures (AWS, InfluxDB, TimescaleDB) and deployed user-driven Streamlit dashboards containerized for R&D teams. Luke led Bayesian and probabilistic modeling work—contributing to the well-known PyMC project by improving distributions and inference utilities—and applied Bayesian smoothing to noisy wastewater surveillance data at UKHSA. His work blends rigorous statistical modelling, production-ready engineering, and UX-aware dashboard design, having halved duplicate results in an Elastic-based news platform using SBERT deduplication. He also brings hands-on DevOps and team-upskilling experience, introducing modern Git workflows and shared ETL libraries across teams. Based in Halifax, he combines a scientist's attention to uncertainty with pragmatic data engineering for operational impact.

6 years of coding experience

3 years of employment as a software developer

MChem, Chemistry with Medicinal Chemistry, First Class, MChem, Chemistry with Medicinal Chemistry, First Class at Newcastle University

Doctor of Philosophy - PhD, Physical Chemistry, Doctor of Philosophy - PhD, Physical Chemistry at University of Bristol

Github Skills (8)

statistics10

mcmc10

probabilistic-programming10

python10

statistic10

bayesian-inference10

numpy9

variational-inference8

Programming languages (2)

Jupyter NotebookPython

Github contributions (5)

pymc-devs/pymc

Jan 2022 - May 2022

Bayesian Modeling and Probabilistic Programming in Python

Role in this project:

Back-end Developer & Data Scientist

Contributions:39 reviews, 4 commits, 12 PRs in 4 months

Contributions summary:Luke primarily focused on improving the PyMC library's statistical and probabilistic modeling capabilities. Their contributions include refining the Multinomial and Categorical distributions by addressing issues such as normalization, and handling negative probabilities and symbolic parameters. Further improvements included implementing the logcdf function for truncated normal distributions and implementing probability inference for arc transformations. These changes enhance the library's robustness and functionality for Bayesian inference tasks.

pythonbayesian-inferencestatistical-inferencemachine-learningprobabilistic-programming

LukeLB/pymc

Jan 2022 - Dec 2023

Probabilistic Programming in Python: Bayesian Modeling and Probabilistic Machine Learning with Aesara

Contributions:121 pushes, 7 branches in 1 year 11 months

pythonmachine-learningprobabilistic-programmingbayesian-statisticsprobabilistic-machine-learning

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