Data analyst who builds | Python, SQL, ML, AI systems | MSc Business Analytics and Data Science @ IE University, Madrid
🇮🇪 International 400m sprinter for Ireland (PB 46.95s, European U23 relay finalist)
I take messy real world data, turn it into something a person can act on, and check my own numbers before anyone else has to.
Most of what I build starts with a raw source like CRM records, SEC filings, stock fundamentals, Kaggle datasets or my own training logs. It ends with something a team can use: a forecast, a dashboard, an alert or a ranked list. I build quickly with AI coding tools, then spend the time I save on tests and validation. Two of the projects below were rebuilt after I found errors in my own earlier results, and I published what I found.
- Studying: MSc Business Analytics and Data Science at IE, Madrid. Available for roles from August 2027.
- Building: a tool that drafts security questionnaire answers from a vendor's public trust pages, with a citation for every answer and a flagged gap wherever the evidence is missing
- Learning: Spanish (A2, working toward B1)
Each project follows the same shape: what goes in, what I do to it, what comes out.
🤖 medalist · an AI agent that enters ML competitions on its own
In: a competition bundle (training data, target column, scoring metric, fixed cross validation folds)
Transformation: an AI agent designs and runs experiments. The harness around it enforces the rules the agent can't be trusted to keep itself: a holdout set carved off before the agent sees any data, fixed folds, a time and experiment budget, and a SQLite ledger of every run.
Out: a valid submission and a written report. On Kaggle Playground S6E3 (4,143 teams) it reached an estimated top 15.8% in 17 experiments with no human input.
Stack: Python, LightGBM, XGBoost, CatBoost, SQLite, pytest (139 tests), GitHub Actions
📈 TradeMetrics · portfolio analytics you can check line by line
In: daily portfolio values, a trade log and benchmark prices. The repo uses synthetic data; my own account stays private.
Transformation: computes Sharpe with confidence intervals, Sortino, CAPM alpha and beta, Value at Risk, and long and short FIFO profit and loss net of commissions.
Out: a single file HTML report, plus a notebook that proves every metric against simulated data where the true answer is known in advance.
Why it exists: my first version got Sortino, alpha, VaR and FIFO wrong. I rebuilt it test first (79 tests) and removed the Sharpe ratio I used to quote, because one year of returns can't support it.
Stack: Python, pandas, NumPy, SciPy, pytest, Jupyter, GitHub Actions
🏃 OptimalAthlete · the model that taught me about data leakage
In: training sessions, wellness metrics and race results for 400m sprinters
Transformation: builds 12 features over real calendar windows, trains Random Forest and XGBoost, and scores them with walk forward validation against a simple "recent average" baseline.
Out: a Streamlit dashboard and an honest verdict. My first version claimed R² of 0.84. When I audited it, the model was recognising which athlete was racing, not predicting form. I retracted the number and built a demo: on data with zero signal, the old method still reports R² of 0.906.
Stack: Python, sklearn, XGBoost, SQLAlchemy, Streamlit, Plotly, pytest (31 tests), GitHub Actions
🔎 GrowthHog · a weekly screener for 170+ growth stocks
In: fundamentals, prices and news from Polygon, with FMP and yfinance as fallbacks
Transformation: computes 8 trailing twelve month metrics, puts each company in a lifecycle stage (Early, Hypergrowth, Scaling, Maturing) and reweights its score to match. Sanity caps and a cyclical sector adjustment stop one off events from looking like quality.
Out: a 0 to 100 score per company, an HTML report and a Dash dashboard, refreshed weekly on cron
Stack: Python, pandas, Dash, Polygon API, Anthropic API
The code for these is private because it runs on client data. Happy to walk through any of them.
Fund Recs, Dublin · fund reconciliation FinTech · GTM Systems and Analytics Intern, 2026
- Churn early warning: product telemetry + CRM + revenue records → the company's first account by month table → a Kibana dashboard showing how far ahead usage drops signal revenue decline
- Pipeline forecast: 707 resolved CRM deals → a forecast rebuilt in SQL → adopted by the CRO
- Compliance answers: internal policy and due diligence documents → a RAG system → answers in seconds instead of a manual search
Leaving Cert Plus · EdTech for Ireland's school leaving exams · freelance AI and technical consultant, 2026 to now
- Maths verification engine: AI generated exam questions → a deterministic checker validates each one → only verified questions reach students (content costs down about 50%)
- Content pipelines: three AI generation pipelines for information subjects, languages and maths
- Email CRM: replaced Mailchimp with a custom EU hosted CRM (platform costs down 60%+)
Side build: an SEC filing monitor that watches EDGAR for new filings, flags odd lot tender offers and sends Telegram alerts. Deployed on Railway.
