Chris Hughes
Data Science AI Engineering Manager (Retail & Consumer Goods)
London, England, United Kingdom
Summary
🤩
Rockstar🎓
Top SchoolChris Hughes is a Data Science and AI Engineering Manager based in London with 8 years of experience turning ambitious, stalled AI initiatives into production for retail and consumer goods at scale. At Microsoft he co-founded a delivery studio for EMEA customers and now leads multi-disciplinary teams responsible for AI strategy, technical standards, and end-to-end delivery across retail, telecoms, media and gaming. He blends hands-on architecture and experimentation—believing early design choices determine outcomes—with pragmatic program leadership that moves projects from vague goals to shipped systems. His background ranges from building visual search and recommender systems at Marks & Spencer to shaping AzureML tooling, and he’s known for hiring and coaching inclusive, delivery-focused teams. Outside work he’s a guitar enthusiast and regular gym-goer, reflecting a practical, craft-oriented approach both to engineering and life.
8 years of coding experience
7 years of employment as a software developer
Master’s Degree Information Technology, Master’s Degree Information Technology at University of York
Github Skills (154)
apache-spark10
python10
anchor10
model-management10
detectors10
deep-learning10
gpu10
memory-mapped-file10
gpu-acceleration10
yolov710
rows10
efficientnet10
acceleration10
mlflow10
onnx10
Programming languages (7)
Github contributions (5)
Chris-hughes10/Yolov7-training
Sep 2022 - Nov 2022
A clean, modular implementation of the Yolov7 model family, which uses the official pretrained weights, with utilities for training the model on custom (non-COCO) tasks.
Contributions:2 releases, 1 review, 14 commits in 2 months
weightscocoobject-detectioncleanyolov7
Chris-hughes10/pytorch-accelerated
Oct 2021 - Jan 2023
A lightweight library designed to accelerate the process of training PyTorch models by providing a minimal, but extensible training loop which is flexible enough to handle the majority of use cases, and capable of utilizing different hardware options with no code changes required. Docs: https://pytorch-accelerated.readthedocs.io/en/latest/
Contributions:34 releases, 6 reviews, 104 commits in 1 year 3 months
casesno-codetrainingpytorch-modelstransformers
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