Daniel Usvyat
Dubai, Dubai, United Arab Emirates
Summary
🤩
Rockstar🎓
Top SchoolDaniel Usvyat is an experienced ML engineer and data scientist with eight years of building production-ready AI systems, currently based in Dubai. He combines hands-on model development and deployment—contributing to MindsDB’s recommender pipeline and packaging—with a consulting background that delivered multi-million pound cost benefits for enterprise clients. At Capgemini he led analytics teams, built production recommendation engines and automation tools, and presented proof-of-concept work at Dreamforce, demonstrating an ability to turn prototypes into business impact. He also co-founded a fitness startup during his MSc at UCL, showing early product and go-to-market instincts alongside technical depth. Known for preferring working systems over slideware, he spans the stack from data engineering and model preprocessing to deployment and config management.
9 years of coding experience
2 years of employment as a software developer
Bachelor of Science (BSc) Biochemistry with Bioinformatics & Statistics, Bachelor of Science (BSc) Biochemistry with Bioinformatics & Statistics at University College London, U. of London
Github Skills (12)
data-preprocessing10
pandas10
machine-learning10
scipy10
python10
recommender-system10
ai9
pydantic9
setuptools8
database7
databases7
ag4
Programming languages (4)
Github contributions (5)
mindsdb/mindshub
May 2023 - Jan 2026
The unified workspace where open-source models get things done for you.
Role in this project:
Data Scientist & ML Engineer
Data Scientist & ML EngineerContributions:1 release, 396 reviews, 249 PRs in 2 years 9 months
Contributions summary:Daniel's commits focus on the preprocessing of recommendation data and the creation of machine learning models within the MindsDB project. They're actively involved in developing components for a LightFM-based recommender system, suggesting a data science/ML engineering role. Their work includes building helper functions to handle data encoding, matrix construction and item mapping to build a production-ready model, indicating a focus on practical machine learning implementation. Additionally, contributions involve setting up required configurations with setuptools, which suggests development and deployment skills.
localaimachine-learning-databasesolacesinglestoreexplainable-ml
mindsdb/email_rag
Feb 2024 - Sep 2024
T.I.M.E: Thoroughly Intelligent Mail Explorer" Repo to try and build an incredible RAG system over email (this is to test the SOTA in RAG)
Contributions:30 reviews, 26 PRs, 59 pushes in 6 months
e-mailaiemailllmrag
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