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Mathias VillerabelMV

Mathias Villerabel

Data scientist

900 €/jour
Paris, FR
3-7 ans

Délai de réponse moyen : 1h

À propos de Mathias

  • Data scientist and research engineer with 6+ years across consumer goods, geospatial analytics, and finance. I build reliable ML models and large-scale data pipelines and translate business needs into production systems. Comfortable in multicultural teams (FR/CH/JP
  • Français

    Bilingue ou natif

  • Anglais

    Bilingue ou natif

Accepte de travailler sur site
Paris (jusqu’à 50 km)

Expériences

  • SwissRe
    Data Scientist
    BANQUE & ASSURANCES
    septembre 2023 - mai 2025 (1 an et 8 mois)
    Zurich, Suisse
    • Built IFRS 17 actuarial/financial simulation engine (cash-flows, discounting, contractual service margin) to benchmark EY consulting outputs and ensure regulatory compliance.
    • Automated end-to-end reconciliation (GL ↔ actuarial models), reducing manual work from multi-day reviews to <2 h/run and improving auditability & reproducibility.
    • Designed and optimized PySpark pipelines in Palantir Foundry, scaling to 10⁹+ records/batch and integrating with downstream financial reporting systems.
    Python Palantir Foundry PySpark CI/CD Cloud computing
  • Pernod Ricard
    Data Scientist
    VINS & SPIRITUEUX
    janvier 2023 - août 2023 (7 mois)
    Paris, France
    • Built time-series forecasting and ML models (ARIMA, gradient boosting) and designed a custom PyTorch framework for scalable training/inference, improving 3-month demand forecasts by 12% vs. FA baseline.
    Python Machine learning Forecast Pytorch MLflow
  • Synspective
    Applied Scientist
    AÉRONAUTIQUE & AÉROSPATIALE
    décembre 2019 - janvier 2023 (3 ans et 1 mois)
    Tokyo, Japon
    • Automated detection of new construction from SAR time series (InSAR coherence + intensity), improving urban growth monitoring.
    • Delivered a cloud-based Earth observation platform with dynamic caching on Google Cloud, reducing data latency for SDG indicators in economics and environment.
    • Built scalable object detection models for maritime trade, monitoring container, car, truck, and ship flows; awarded 2nd Prize at the NEDO Challenge.
    • Applied Earth observation and machine learning methods during NASA, ESA & JAXA hackathons to address real-world challenges such as wildfire monitoring and prediction.
    Python Data science satellite Google cloud Deep Learning

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Formations

  • M.Sc. Computer Science
    Sorbonne University France
    2019
    Machine learning, agents, robotics, operational research, decision
  • B.Sc. Computer Science & Mathematics
    Sorbonne University
    2016
    Algorithmic, Statistics, Software Engineering, Network, Linux

Compétences

Catégories