Daniel Petrov is a data scientist with 8 years’ experience combining academic rigour (MPhil, Data Intensive Science, Cambridge; First-class BSc in Computer Science & Statistics) with hands-on work in quantitative trading, NLP and cloud engineering. He has built production ML pipelines and real-time data infrastructure for algorithmic trading as a co-founder, deployed NLP summarisation systems at Equinor, and engineered resilient AWS-backed services during a payments migration at Tripadvisor. Comfortable in fast-paced, deadline-driven environments, he blends high-performance computing (Numba, QuestDB, KDB+/Q) with practical model work (LSTMs, transformers, classical ML) to close the gap between research and production. Based in Oxford and internationally minded, he brings both startup grit and institutional trading discipline to teams seeking measurable, scalable ML solutions. An interesting thread across his roles is a focus on automating repeatable data workflows—whether for live trading, news summarisation or subscription backups—so models reliably serve business objectives.
8 years of coding experience
1 year of employment as a software developer
GCSEs: 6A*s, 2As, 2Bs, GCSEs: 6A*s, 2As, 2Bs at Abingdon School
Bachelor's degree, Computer Science, Statistics, First Class, Bachelor's degree, Computer Science, Statistics, First Class at University of St Andrews
Master of Philosophy - MPhil, Data Intensive Science, Master of Philosophy - MPhil, Data Intensive Science at University of Cambridge
A-Levels: Mathematics (A*), Further Mathematics (A*), Physics (A), Biology (A), A-Levels: Mathematics (A*), Further Mathematics (A*), Physics (A), Biology (A) at Magdalen College School, Oxford
Contributions:15 pushes, 1 branch, 1 tag in 2 years 2 months
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