Welcome!

Hi, I’m Alejandro — Data Scientist (PhD-trained)

I work at the intersection of rigorous modelling and practical decision support — building reproducible Python/SQL pipelines and ML systems that turn messy data into outputs people can act on.

My background is in quantitative ecology and climate-change science, where I built uncertainty-aware models to understand how living systems respond to environmental change. In industry-focused data science, I bring the same strengths: careful assumptions, clear validation, and decision-ready delivery.

What I do

I build end-to-end analytics and ML workflows that prioritise reliability and usability:

• define measurable questions (metrics, cohorts, success criteria)
• engineer clean, testable data pipelines with validation and artefacts
• train and evaluate models with transparent trade-offs
• communicate results clearly (what changed, why it matters, what to do next)

Featured work

Job Intelligence Engine — parses thousands of real data job ads into a skills taxonomy and market signals, then recommends best-fit roles and a prioritised upskilling plan.

• Skill demand & salary signals by role/family
• Best-now vs stretch role job recommendations
• Ranked upskilling priorities (positioning lift)

Stack: Python • NLP embeddings • XGBoost/LightGBM • Streamlit • reproducible pipeline & persisted artefacts • SHAP

See demo & details