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MJ

Data and machine learning

Models for decisions that carry money or risk.

A BSc in Mathematical Engineering and four years applying it: predictive models, mathematical optimisation and analysis built for people who have to defend the answer.

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Python
SQL
MongoDB
Pandas
NumPy
Scikit-learn
Power BI
Tableau
Google Analytics
Azure
AWS
Jupyter Notebook
Python
SQL
MongoDB
Pandas
NumPy
Scikit-learn
Power BI
Tableau
Google Analytics
Azure
AWS
Jupyter Notebook

Predictive modelling

Models built across business and public service domains, on datasets from a few thousand rows to whatever the client actually has, with the validation written down.

Mathematical optimisation

Scheduling and routing problems solved as optimisation rather than heuristics. I built the night route planning models for Metro de Madrid.

Quantitative finance

Fund analysis, risk and return assessment, and selection and rebalancing algorithms, applied across roughly ten real portfolios in BrokerAI.

Pipelines and reporting

The ETL that gets data out of spreadsheets and into a database it can be queried from, plus the executive dashboards that make it worth reading.

Where I have shipped this

  • BrokerAI

    Proof of concept for a private banking institution

    An AI assistant that analyses and manages long-term investment fund portfolios: quantitative fund analysis, risk and return assessment with predictive models, and selection and rebalancing algorithms.

    Applied across roughly ten real portfolios · Still advising the institution occasionally

  • Public sector AI

    DGT, Metro de Madrid and Madrid City Council, at Accenture

    Three years leading AI applied to data analysis for major public sector clients: predictive models, generative AI embedded in client processes, and RAG systems with semantic retrieval.

    Optimisation models for Metro de Madrid night routes · AI for automated recruitment screening

  • TAJO

    Open

    Maintenance operations, deployed

    A light self-hosted maintenance management system covering workshop, warehouse, shift and work orders, with an ETL that migrated the existing spreadsheet inventory into a real database.

    641 inventory rows migrated out of spreadsheets · Barcode scanning and biometric sign-in on the floor

  • Monero

    Own product, in production

    Finance and invoicing for a mixed tax situation: employee and self-employed at once. The business side and the personal side live together without mixing, and logging an expense costs one photo.

    Installable PWA with passkey sign-in · Receipt capture backed by a vision model

Tell me what is slow, manual, or risky.

I read every message myself and reply within one working day.

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