Louise Deason
Technical Program Manager at Fundamental
London, England, United Kingdom
Summary
🤩
Rockstar🎓
Top SchoolLouise Deason is a Technical Program Manager with 11 years of experience blending Big Tech rigor from DeepMind and Facebook with startup agility, currently driving programs at Fundamental from London. She excels at navigating the grey zone where process meets innovation, turning research-grade projects into production-ready outcomes and clarifying decisions for cross-functional teams. Her hands-on engineering roots include backend and data-focused contributions to high-profile open-source DeepMind research tooling, showing practical experience with dataset engineering and metadata infrastructure. While studying for an MSc in Software Systems & Security at Oxford, she writes the newsletter Technically Feasible about surviving the tech industry and spends downtime at racetracks—evidence of a pragmatic, performance-oriented mindset.
11 years of coding experience
7 years of employment as a software developer
Heathside SchoolMaster of Science - MSc Software Systems & Security, Master of Science - MSc Software Systems & Security at University of OxfordRace Technician Motorsport, Race Technician Motorsport at National College for MotorsportActing, Acting at Brooklands CollegeBSc Computer Science, BSc Computer Science at Birkbeck, University of London
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (7)
python10
data-management9
bash9
data-pipeline8
data-pipelines8
sqlalchemy8
cloud-storage7
Programming languages (2)
Github contributions (5)
google-deepmind/deepmind-research
Aug 2019 - Jul 2021
This repository contains implementations and illustrative code to accompany DeepMind publications
Role in this project:
Back-end Developer
Back-end DeveloperContributions:29 commits, 74 pushes in 1 year 11 months
Contributions summary:Louise primarily contributed to the `google-deepmind/deepmind-research` repository by adding a script (`download.sh`) and a Python file (`metadata_schema.py`) related to the "sketchy" dataset. The `download.sh` script automates the process of downloading dataset shards from cloud storage. The `metadata_schema.py` file defines the SQLAlchemy schema for a metadata database associated with the dataset. These contributions suggest a focus on data management and potentially backend data processing or infrastructure.
pytorchimplementationsdeep-learningneural-networksmachine-learning
google-deepmind/abstract-reasoning-matrices
Jul 2018 - Feb 2019
Progressive matrices dataset, as described in: Measuring abstract reasoning in neural networks (Barrett*, Hill*, Santoro*, Morcos, Lillicrap), ICML2018
Contributions:4 commits, 1 PR, 2 pushes in 6 months
pytorchmatriceshilldeep-learningdataset
Find and Hire Top DevelopersWe’ve analyzed the programming source code of over 60 million software developers on GitHub and scored them by 50,000 skills. Sign-up on Prog,AI to search for software developers.