Summary
Matthew Spike is an architect-level consultant and founding-engineer with a PhD in cognitive science who turns formal semantics and standards into practical, auditable data systems and applied AI/NLP solutions. Over a decade he has built reproducible, contract-first pipelines and warehouses (UFSA v2) that convert specs into vendor-neutral, joinable tables for DuckDB/Postgres/BigQuery/Snowflake, combining deterministic flows with LLM-guided extraction. He brings deep hands-on expertise in Python, PyTorch/Transformers, probabilistic methods, SBOM ingestion, FHIR/ISO standards and reproducible builds, plus MLOps and cloud deployment experience. A former university lecturer and research supervisor, he pairs clear documentation and executive briefings with rigorous provenance and quality signals—useful for teams wrestling with schema sprawl, compliance, or interoperable reference data. Based in Edinburgh, he’s available for architect-level roles, founding-engineer work, or short pilots to quickly convert standards into governed, auditable tables.
10 years of coding experience
Doctor of Philosophy - PhD, Cognitive Science and Linguistics, Doctor of Philosophy - PhD, Cognitive Science and Linguistics at The University of Edinburgh
Bachelor of Arts - BA, Linguistics and Turkish, 1st class honours, Bachelor of Arts - BA, Linguistics and Turkish, 1st class honours at SOAS University of London
English, Turkish, Spanish