À propos de Dag
I take data projects from idea to production. I start by assessing whether ML is actually justified, define KPIs, build POCs and MVPs, and deploy. Most of my work is early-stage turning unclear problems into working systems, often across teams. I make sure non-technical stakeholders understand what the results mean and how to use them.
Based in Toulouse, open to occasional on-site if needed.
Anglais
Bilingue ou natif
Français
Capacité professionnelle complète
Letton
Bilingue ou natif
En télétravail uniquement
Travaille majoritairement à distance
Expériences
- Alia SanteData ScientistSECTEUR MÉDICALdécembre 2025 - avril 2026 (4 mois)Toulouse, FranceImplemented time series synthetic data generation methods based on state-of-the-art research and built a VAE-based experimentation and optimisation pipeline.Dev: Python, Git, PyTorch, MLflow, Optuna
- Proximus AdaData ScientistTÉLÉCOMMUNICATIONSfévrier 2022 - août 2024 (2 ans et 6 mois)Brussels, BelgiumDesigned data-driven solutions improving operational efficiency and innovation for the biggest Belgian telecommunications provider. Assessed project feasibility and actionability, defined KPIs and worked on discovering new opportunities with stakeholders. Maintained and developed E2E ML pipelines.Projects — Production•Photo Classification: Automated manual review of servicephotos reducing the manual review workload and enhancingservice quality. Finetuned a pre-trained base model, deployedas an API for system integration and real time-prediction, andmaintained a Dash app monitoring dashboard.•AI-Powered Web Scraping: Maintained a ChatGPT-poweredweb scraper, automating information retrieval significantlyreducing manual workload.•Time-series prediction: Maintained a time-series-basedprediction pipeline for estimating fuel tank levels, optimisingrefuelling.•Cell Tower Fault Detection: Analysed cell tower data toidentify anomalies and support pre-emptive fault detection.Projects — Prototypes•Call Routing: Built a classifier to match callers with appropriateagents. Integrated with routing system.•Automated Ticket Sorting: Developed ML + LLM-assistedclassification for support tickets.Infrastructure•Azure ML / Azure MLOps•OnPrem: SQL Data Warehouse, DataLake, Oracle, AirFlow,MLFlow, SparkAdditional responsibilities•MLOps advocacy and research: Promoted MLflow adoption tostandardize and streamline model development.•ML training: Conducted sessions for business coordinators todemystify AI concepts and foster informed decision-making.•Company Culture Initiatives: Managed activities andworkshops for defining and promoting company values.
- Toyota Motor EuropeData Science - Master's thesisAUTOMOBILEjanvier 2021 - août 2021 (7 mois)Brussels, BelgiqueAnalysed Automatic Driving System usage, focusing on timeseries pattern discovery and classification using the SAX method.Built supporting databases and PowerBI dashboards forvisualization. (7 months)Dev: Python, Git, Jupyter, Pycharm, Dask, PowerBI
Recommandations
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Formations
- Master's ThesisToyota Motor Europe2021Master's Thesis
- Generative AI with Large Language Models DeepLearning.AIAmazon2024Generative AI with Large Language Models DeepLearning.AI