Behavioural data scientist — I build data systems that show their work, so a complex and opaque reality becomes legible for the people who actually have to act on it.

What I care about isn't the model — it's whether the output can be trusted: does it cite its source, does it know how sure it is, and does it fail loudly instead of overselling. I work at the intersection of behavioural science, rigorous measurement, and dependable data engineering.

A note on AI, since everyone claims it: in my projects the model is rarely more than a thin layer — deliberately. The deterministic engine decides; the model phrases. The work I actually do is the unglamorous part underneath: pipelines, measurement, provenance, and evaluating when a system is genuinely ready rather than good-on-average.

I hold a postgraduate Master's in Behavioural Data Science (IL3 – Universitat de Barcelona; Honours, 4th in cohort). Before specialising, I spent nearly three years as a Business Analyst at Santander, as the pre-production QA owner for a global platform — where I learned institutional rigour by being the last check before a rollout that would have miscalculated credit limits for thousands of suppliers.

• Political-data observatory — a reproducible pipeline that integrates independent European sources (economic, electoral, party-level) into one traceable dataset, reconciling entity IDs across them so provenance survives the integration. (My main line of work.)
• Retrieval systems you can trust — a retrieval (RAG) system with its own evaluation harness and a deterministic regression gate (it hard-fails if retrieval quality drops), so the system catches its own regressions instead of finding out in production. (Private — happy to walk through it.)
• Responsible AI in real apps — an endurance training and nutrition platform built end-to-end (FastAPI, PostgreSQL, Supabase, Garmin/Strava, ~2,400 automated tests), where a deterministic engine makes every decision that matters and the model only phrases it. (Private — available on request.)
• Geospatial for decisions — a dashboard built for a UN ESCWA assignment over Lebanon (Leaflet.js, 1,611 ADM3 localities), with a rule-based demographic classifier and prompt-level defenses so answers stay grounded in the data.
• Making data legible — end-to-end analytics for Project RYSE in R (clustering, Random Forest, XGBoost, GLM, ETL), surfacing a decision gap rather than a skill one; and a World Happiness Streamlit dashboard for cross-country wellbeing.

Python · R · SQL · FastAPI · PostgreSQL · Leaflet.js / GIS · Streamlit · R Shiny · Git

Roles where data has to be trustworthy to matter — reliable systems, honest measurement, provenance without overselling causality. That spans information integrity & accountability, evidence & impact, and behavioural insights, in teams that publish their methods and welcome scrutiny. Fully mobile across the EU.