Charles is a data scientist with six years of experience designing and scaling machine learning solutions on large, complex datasets, including satellite imagery, sensor time series and spatial data. He holds a PhD in agricultural and environmental sciences and has worked on multi-partner research programmes in Belgium and New Zealand, combining hands-on engineering with communication to business, technical and scientific stakeholders.
💼 Professional experience
Agilytic
Consultant, AI & Strategy
Since August 2026
DairyNZ (Hamilton, New Zealand)
Invited scientific collaborator, data scientist and data engineer 2026
Satellite data platform for dairy sector monitoring
- Designed and implemented an end-to-end ETL pipeline to ingest, harmonise and store Sentinel-1 and Sentinel-2 data for sector monitoring and modelling.
- Integrated the satellite data workflows into a Posit Workbench environment with Snowflake storage.
- Automated acquisition, preprocessing and structuring of the data for downstream analytics and modelling teams.
- Worked with researchers and platform engineers on scalability, data governance and operational robustness.
- Contributed to exploratory modelling combining spatial, environmental and sensor-derived indicators.
University of Liège
Senior data scientist, Interreg project Holicow 2023 - 2025
- Designed, trained and evaluated models on more than 41 million high-dimensional, multimodal records covering time series, sensor and spatial inputs.
- Built reproducible data pipelines from acquisition and preprocessing through feature engineering and inference.
- Implemented anomaly detection, predictive models and automated analytical workflows.
- Presented results to project managers, domain experts and operational partners, and coordinated technical activities in steering committees.
Data scientist and doctoral researcher, Roadstep programme 2019 - 2023
- Developed predictive models combining remote sensing imagery, meteorological data and environmental time series to estimate available forage on Walloon pastures, feeding the core of a decision support system.
- Integrated multi-source data, ran quality control and structured the analytical workflows.
- Diagnosed inconsistencies in units and acquisition methods across a database assembled by several partners, preventing models from fitting noise rather than signal.
- Worked directly with researchers, engineers and end users to translate operational and scientific needs into data solutions.
🦾 Certifications, trainings
Methodologies
- Machine learning & deep learning
- Anomaly detection
- Predictive modelling & model evaluation
- Remote sensing (optical & radar), GIS
- Multimodal data fusion
- ETL pipeline design
- Reproducible pipelines, model & dataset versioning
- Data quality & traceability
Software & programming
- Python (scikit-learn, XGBoost)
- R
- SQL
- MATLAB
- bash
- git
- Docker
- Snowflake
- Posit Workbench
🎓 Academic credentials
- PhD in agricultural and environmental sciences Université de Liège, 2023
- MSc in Bioengineering, environmental sciences Université de Liège, 2019
🇺🇳 Languages
🇫🇷 French: native
🇬🇧 English: fluent (C1)
🇳🇱 Dutch: basic (A2)
🔐 Proprietary and confidential. 💡 Profile CVs are provided for illustration purposes. ✅ Planning of specific experts will be secured upon formal agreement.