Lead Data Scientist · AI Engineer · Professor · Mathematical Engineer
Working between the United Kingdom and Portugal · Python (typed, NumPy-first) · production AI + reproducible research
"Knowledge is knowing a tomato is a fruit; wisdom is not putting it in a fruit salad." — Miles Kington
I build production systems that turn complex data into reliable decisions, and reproducible research pipelines that turn open data into auditable evidence. My work spans end-to-end AI systems (LLMs, RAG, and agents), forecasting and anomaly detection, and a growing body of econometric and dynamical-systems research on inequality, wealth, and public policy. Across all of it the constants are the same: lean models, robust software practice, and results you can reproduce and defend.
- Production AI & LLM systems
RAG pipelines, agentic workflows, structured outputs, evaluation loops, and audit-friendly narrative reporting — designed for reliability, observability, and CI from the start. - Data science & statistical modelling
The full toolkit: supervised and unsupervised learning, causal inference and experimentation, survival analysis, Bayesian modelling, and robust/heavy-tailed statistics.
With a working emphasis on the parts most people skip — uncertainty quantification and calibration, class imbalance, interpretability and fairness, leakage and drift checks, and honest model selection under real-world noise. - Forecasting & anomaly detection
Classical and foundation-model time series (SARIMAX/Prophet through Chronos and masked-patch transformers), conformal prediction intervals, change-point and rare-event detection, and drift monitoring for operational and sensor-driven systems. - Econometrics & reproducible policy research
Panel and causal models, event studies, synthetic control, and Monte Carlo simulation over open economic data (Eurostat, OECD, AMECO, WID, INE/PORDATA, FRED). - Dynamical systems & mathematical modelling
Attractor dynamics, dynamical-systems econometrics, extreme-value and rare-event methods, and exact algorithmic solvers. - Data & ML engineering
Contract-linked ingestion, dataset curation, streaming and lakehouse patterns, and reproducible project scaffolding.
→ Full stack and model families on the Methods tab.
- Production RAG and agentic systems with evaluation, observability, and CI baked in
- Reproducible econometric research on inequality, wealth concentration, and public policy — including dynamical-systems models of rentier equilibria and crisis dynamics
- Robust forecasting, anomaly detection, and drift control for operational data
- Reusable research infrastructure: contract-linked ETL, GitHub Actions tooling, and reproducibility contracts across multi-paper programmes
Six projects that cover the range. The full catalogue — around 40 repositories across AI, ML engineering, data engineering, statistics, economics, algorithms, and tooling — is on the Projects tab.
- feedback-intelligence-agent — Production-style RAG system: a customer feedback intelligence agent with FastAPI, evaluation, observability, and CI.
- ragops-lab — Evaluation-first RAG and LLMOps platform for production-grade document QA: tracing, regression testing, and cost-aware experimentation.
- clinic-forecasting-platform — Healthcare demand-forecasting and staffing platform: a 13-model benchmark (SARIMAX, Prophet, gradient boosting, Nixtla, Chronos) with conformal intervals, rolling-origin backtesting, and FastAPI serving.
- transaction-risk-lakehouse — Production-style PySpark lakehouse for transaction-risk modelling, fraud detection, and temporal model validation.
- poverty_neoliberalism_research_program — Agent-first scaffold for a ten-paper empirical programme on poverty, wages, taxes, and asset power in the US and UK since 1950, sharing one pipeline and reproducibility contract across all papers.
- bmssp ⭐ — Deterministic Single-Source Shortest Paths solver for directed graphs with non-negative weights, using a BMSSP-style divide-and-conquer design (typed, tested).
Live dashboards: Portugal Economic Indicators · NASDAQ Stock Analytics
I research inequality and political economy, applied econometrics, dynamical systems, production AI, time series and anomaly detection, and survival analysis — mostly organised as multi-paper programmes with a shared pipeline and reproducibility contract. I teach mathematics and data subjects at ESMAD (Instituto Politécnico do Porto) and run seminars on MLOps, streaming analytics, experimentation, and forecasting.
→ Research & Collaboration for research themes and what I am open to working on.
→ Teaching for courses, workshops, and course repositories.
When reaching out, include a short note on your use case, constraints, and timeline so we can assess fit quickly.
- Production AI: shipped RAG and agent systems (
feedback-intelligence-agent,ragops-lab,ai-incident-analysis-agent) — with evaluation, tracing, observability, and CI treated as first-class. - ML & data engineering: built serving, MLOps, and lakehouse/streaming platforms (
fastapi-ml-platform,feature-store-lab,transaction-risk-lakehouse,pyflink-fraud-detection-streaming,llm-data-platform) spanning inference, drift monitoring, feature parity, and contract-linked ingestion. - Research programmes at scale: launched a cluster of reproducible econometric and dynamical-systems projects on inequality, wealth, and policy, several sharing a single pipeline and reproducibility contract across many papers.
- Algorithms & methods: released and refined typed, tested solvers and libraries (
bmssp,min_ratio_cycle,heavytails,dynamical_systems_econometrics). - Reusable infrastructure: built developer tooling (
smart-todo-action,git-actions-collection,article-reminders) to keep a large, multi-repo research output reproducible and maintainable.
Contribution activity across public and private work:
