Data Scientist · AI Builder · Ireland
I build agentic AI systems that turn complex data into decisions — regulatory RAG agents, natural language dashboards, and automated data pipelines. A few examples below.
About
I build end-to-end AI systems — from raw data pipelines through to dashboards and agentic natural language interfaces. The output is always working software: something people can use to make better decisions faster, not a prototype that needs six more months of work.
The core of what I do is turning operational data into decision-support tools: RAG systems grounded in real document corpora, multi-agent workflows that automate structured reasoning, and dashboards that let people ask questions in plain English and get answers with charts. I've applied the same engineering approach across pharma, property, finance, and logistics.
I also have a strong data science foundation — three years of contract work in sales forecasting, time series modelling, predictive analytics, and dashboard reporting before moving into AI engineering full-time. That background means I'm comfortable with both the analytical and the engineering side of a problem.
Before data science, I spent two decades translating complex technical concepts for non-technical audiences — a skill that now shapes how I scope projects, communicate with stakeholders, and build tools that people actually use. I bring the same clarity to a client boardroom as I do to a codebase.
Tools & Technologies
Approach
Understand the business problem, identify what data exists, and define what "done" looks like — before writing a line of code.
End-to-end delivery: data pipelines, AI agents, APIs, and interfaces. Working software, not slide decks or prototypes that need six more months.
Two decades of translating technical complexity into clear language. I speak to engineers, stakeholders, and boardrooms with equal fluency.
Projects
Working systems I've built, all publicly accessible. They reflect the kind of AI engineering I focus on — specific, grounded, and practically useful.
01 / Property Analytics
Live market data · AI agent · AutoML forecasting
A live data pipeline ingesting and transforming transaction records from the national Property Price Register into 8 KPI cards, interactive charts, and an AutoML forecasting model — updated in real time. On top of that sits an AI agent layer: ask questions in plain English and get back generated Plotly charts or written market analysis, powered by Claude Opus 4.6. Fully accessible via the link above — no API key required.
Architecture
02 / Pharma Regulatory
RAG · Document grounding · Compliance benchmarking
A RAG system grounded in ~60 ICH, FDA, EU GMP, and EMA regulatory documents. Designed for scientists and regulatory affairs teams who need precise, citable answers from a large document corpus — not confident hallucinations. Every answer carries a confidence score and exact section citations. Fully accessible via the link above — no API key required.
Architecture
03 / Energy Forecasting
Time series ML · Weather integration · Agentic monitoring
An end-to-end ML forecasting pipeline using real solar panel production and household energy consumption data. Multiple models (Prophet, XGBoost, LightGBM) compared via walk-forward validation with exogenous weather features from Open-Meteo. An agentic monitoring layer detects model drift, flags anomalies, and provides natural language energy management recommendations.
Architecture
Contact
I'm always happy to talk through how these systems work under the hood — architecture decisions, RAG strategies, or agentic design patterns. If you're building something similar or want to collaborate, drop me a line.
micl.brett@gmail.comBased in Ireland