À propos de Thomas
Most companies don't have a data problem, they have a decision problem. Reports get built, dashboards opened, and actual choices still get made on instinct. I turn data into decisions your teams can act on.
Where your data likely loses value
- Reporting describing the past, not the next move: dashboards nobody acts on, KPIs disconnected from decisions. The reporting exists, the insight doesn't.
- Decisions made blind on things that are predictable: the signal is usually present in your data; nobody has modelled it, or the model was never trusted enough.
- Much effort and time spent on the plumbing (extracting, cleaning and reconciling data) instead of analysis).
My approach
- Business first, technology second: I start with your context, constraints and objectives. The analysis is scoped to a decision you have to make.
- Diagnostics: where numbers come from, their usefulness, which questions are answerable now and which need better data.
- Modelling: from descriptive analysis to predictive models - validated, interpretable, and calibrated to needs.
- Built to run: automated pipelines, reproducible outputs, documentation; delivered so your team can own it.
- Autonomous, from business question to business decisions.
Recent projects
- Quality score prediction (product data): random forest regression, driver identification and SHAP interpretation — 1.3% MSE on test set, enabling automated rating and classification.
- ETL pipeline & financial BI dashboard (SQL, Power BI): end-to-end: ingestion, modelling, dashboard generation for performance and risk monitoring.
- Hedge fund-grade systematic trading infrastructure (Python): data acquisition, DuckDB/parquet storage, custom backtesting engine and walk-forward optimization, deployed for live execution.
- Equity screening engine (production, daily runs): supervised classification across global equity markets, data acquisition, parallel computing, automated deployment.
Github: ThomasLaloux
Anglais
Bilingue ou natif
Français
Bilingue ou natif
Néerlandais
Notions
Accepte de travailler sur site
Brussels (jusqu’à 10 km), Namur (jusqu’à 10 km)
Expériences
- Self-employedData Analyst & Workflow/AI Automation Developerseptembre 2025 - Aujourd'hui (11 mois)- Data analysis and predictive modeling (python): exploratory data analysis, calibration and deployment of a XGBoost predictive model for product quality assessment- Data analysis (SQL, Power BI): ETL pipeline and financial dashboard- AI/workflow automation (python, n8n): business-specific document generation under constraints - multi-agentic system integrating RAG, APIs, specialized prompts, GenAI (local SLM/cloud LLM), n8n orchestration, allowing to save 20-40h a month- Quantitative development (python): architectural design and implementation of a production-grade/scalable systematic trading infrastructure - custom backtesting engine, live trading, monitoring of performance & risk metrics
- Self-EmployedProprietary trading & quantitative developmentjanvier 2018 - novembre 2025 (7 ans et 10 mois)- Eight years running my own book and building the systems behind it, by choice, not as a client-service practice.Developments now largely complete, opening to client work in 2026.- Data analysis & equity market screening (matlab): performed data analysis and supervised classification (comprehensive screening engine) to identify position trading opportunities across global equity markets on a daily basis (data acquisition with APIs, trend following strategies, custom visualization, parallel computing, deployment in production.- Algorithmic trading (python, mql5): developed and backtested mid-frequency algorithmic trading systems (long/short trend following, mean reversion), coded advanced day trading indicators to support discretionary
- ENGIEBusiness & Market Analystjuillet 2012 - décembre 2017 (5 ans et 5 mois)Brussels, Belgium- Data analysis (SQL, python): applied SQL queries and ML classification techniques to cluster of clients behavior from smart thermostat data and produce energy efficiency insights.- Power market modelling: modelled the LT Italian supply, demand and power prices as a key driver for strategic positioning and investment files.- Consulting in economic modelling (Excel): designed total cost of ownership models comparing green mobility solutions across energy types. Cooperated with M&A teams on assets valuations. Managed budgets, relationships with internal clients, and VIEs.- Market analysis: studied green mobility market potential, competition & regulation.
Recommandations
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Formations
- Diploma of Advanced Studies in StatisticsUCLouvain2006Diploma of Advanced Studies in Statistics
- MSc. in StatisticsUCLouvain2005MSc. in Statistics