Jack Millichamp is a Senior Data Scientist at Royal Mail with six years' experience applying statistical rigour and modern ML—especially deep learning with PyTorch/Keras/TensorFlow—to proof-of-concept projects that aim to transform a 500-year-old organisation. He combines strong Bayesian and classical statistics, SQL/BigQuery proficiency and GCP deployment skills (Kubernetes, Vertex AI, Docker) to take NLP and image-processing ideas from prototype to demonstrator. Educated in theoretical physics (BSc) and data science (MSc), Jack pairs academic curiosity with practical engineering, publishing work on ResearchGate and maintaining an active GitHub portfolio. Outside product work he tutors maths and physics extensively, a role that has sharpened his ability to explain complex concepts simply and iterate solutions for varied audiences.
6 years of coding experience
2 years of employment as a software developer
MSc Data Science, MSc Data Science at University of Bath
Bachelor of Science - BSc Theoretical Physics, Bachelor of Science - BSc Theoretical Physics at University of Birmingham
A-Level Mathematics (A*) Chemistry (A) Physics (A); AS Further Mathematics (A) AS Biology (A), A-Level Mathematics (A*) Chemistry (A) Physics (A); AS Further Mathematics (A) AS Biology (A) at Peter Symonds College
A brain-inspired version of generative replay for continual learning with deep neural networks (e.g., class-incremental learning on CIFAR-100; PyTorch code).
A website I created as part of a Theoretical Physics project.
Contributions:12 pushes, 1 branch in 1 year 11 months
physicscsstheoretical-physicstheoreticalbootstrap
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