Ewen Gillies is a Lead Technologist with 11 years of experience bridging high-energy physics research and production-grade ML and data engineering. He leverages a strong mathematical background and a PhD from Imperial College to design cloud-native, cost-conscious data pipelines and performant code—skills honed building infrastructure at Util and now steering technology at Schonfeld. Comfortable across C++, Python, Spark and AWS, he moves projects from R&D to commercial delivery while mentoring engineers and shaping hiring and architecture strategy. An active open-source contributor, he has practical expertise in Python packaging and build systems from work on the tigramite causal-inference library. Colleagues value his ability to adapt quickly across roles and environments, a trait reinforced by international research collaborations on supercomputing and FPGA ML accelerators.
11 years of coding experience
9 years of employment as a software developer
Master of Science - MS Theoretical and Mathematical Physics, Master of Science - MS Theoretical and Mathematical Physics at The University of Edinburgh
Doctor of Philosophy - PhD, Doctor of Philosophy - PhD at Imperial College London
Tigramite is a python package for causal inference with a focus on time series data. The Tigramite documentation is at
Role in this project:
Back-end Developer
Contributions:256 commits, 15 PRs, 38 comments in 2 years 11 months
Contributions summary:Ewen primarily focused on enhancing the codebase, particularly around installation and build processes. Their commits involve modifications to `setup.py` and `setup.cfg` files, indicating a focus on build configuration and dependency management. These changes include optimizing installation procedures, adding required dependencies, and enabling development mode functionality. The user demonstrates a strong grasp of Python packaging and build systems.
Contributions:280 commits, 8 PRs, 106 pushes in 3 years 5 months
cometmachine-learningtracking
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