Chaeyoon
Kim.

Data Scientist at NHS England · Certified AI Ethicist · London

Top skills

Python PySpark Machine Learning AI Engineering Tableau SQL R

Working at the edge of data and health.

I'm a Data Scientist at NHS England working on healthcare workforce modelling and LLM applications. Certified AI Ethicist with a background in data engineering at Samsung Semiconductor and an MSc in Data Science from City St George's, University of London.

LangChain Ambassador (2025) · open to new connections and collaborations.

Based in

London, UK

Role

NHS England

Background

Samsung · City

View full CV ↗

Selected work.

CitySAT poster presented at SMART 2021 / ISWC
1st Place Semantic Answer Type Prediction MSc dissertation submitted to the SMART 2021 shared task at the International Semantic Web Conference. Ranked 1st for classifying the expected answer type — entity, literal, or boolean — from natural-language questions over a knowledge graph.
NHS Policy Navigator proof-of-concept UI design
NHS Policy Navigator Adaptive retrieval agent over the NHS 10-Year Health Plan, built in London. Uses agentic RAG to answer nuanced policy questions with source attribution.
Reducing Missed NHS Appointments ML solution to cut the cost and waitlist impact of non-attended hospital appointments, developed for a hackathon challenge set by the No. 10 data science team.
Heartlink — Heart Disease Prediction Spec-driven heart disease prediction pipeline with an NHS-styled dashboard. Classification, regression, and clustering on UCI Heart Disease data with clinical guardrails and property-based testing.
In Progress PWR Workforce Elasticity Modelling Panel econometrics estimating how NHS provider non-substantive staff spend responds to agency-restriction policy. 68 NHS trusts, 4 financial years, 68 passing tests. Headline elasticity β = −0.287 [95% CI −0.434, −0.140], cluster-robust SE on ICS.
Getting Started with Makaton — AAC Choice Board Digital symbol-based choice board for non-verbal and emerging-verbal pupils in UK SEN schools. React 18 + Supabase, optimised for iPad. Predictive card suggestions via Markov chain and Thompson-sampling bandit — no runtime LLM dependency. GDPR-compliant with multi-source AAC symbol fallback chain.

Thoughts on
data science.

Reflections & project notes

On LinkedIn