Expertise
What I do
Leading a data project means understanding each of its stages. I have worked on all of them, from scoping to code.
Data & AI project management
From scoping to adoption: data projects that actually land.
I turn a business need into a roadmap, align technical and business teams, and keep the focus on value delivered, not on activity.
- Scoping and requirements
- Roadmap and prioritisation
- Cross-functional team coordination
- KPI-driven steering and reporting
- Change management and adoption
Tools
- Agile / Scrum
- Notion
- Advanced Excel
- Power BI
Analytics & Business Intelligence
Reliable metrics that genuinely inform decisions.
Collection, cleaning, modelling, reporting: I build dashboards teams actually use, on data whose quality is known.
- KPI definition and tracking
- Dashboards and data visualisation
- Data cleaning and reliability
- Advanced SQL and modelling
- Reporting automation
Tools
- SQL
- PostgreSQL
- Python (Pandas, NumPy)
- Power BI
- Tableau
- Advanced Excel
Data science & AI engineering
Useful models, built into products rather than locked in a notebook.
From feature engineering to product integration: supervised and unsupervised learning, rigorous evaluation, deployment on infrastructure you control.
- Supervised and unsupervised learning
- Feature engineering and model evaluation
- AI product integration, prompt engineering
- APIs, ETL and data pipelines
- Cloud deployment (Docker, AWS)
Tools
- Python
- Scikit-learn
- Pandas
- Jupyter
- Docker
- AWS EC2
Digital products & software engineering
Robust platforms, designed for the people who use them.
A software engineer by training, I design and ship web applications, acquisition funnels and internal tools, with code built to last.
- Web application design
- Software architecture and databases
- Acquisition funnels and websites
- Infrastructure, networks and support
Tools
- TypeScript
- React
- Java
- Kotlin
- PostgreSQL
- Nginx
Method
My method fits in four steps.
Four steps inspired by CRISP-DM and IBM’s data science methodology, adapted to the pace of a real organisation.
Understand the decision
Who decides what, how often, and on what basis today.
Make the data reliable
Known sources, measured quality, one version of the numbers.
Build with the users
A first tool within weeks, refined with the people who use it.
Measure adoption
The project ends when the tool is used, not when it is delivered.