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Experiences

AI diagnostics to optimize performance and reduce downtime
Integrated ML components for self-service anomaly detection in production
Built and operated GB-TB-scale time-series data pipelines
Deployed hundreds of ML models with inference costs < €1k/month
Developed industrial data-collection partnerships
Building a shared data platform across all product modules

Developed predictive maintenance solutions for high-performance computing (HPC) systems
Delivered customer-facing POCs, from pain-point analysis to ML validation
Mentored two junior data scientists on production-ready ML practices

Industrialized a predictive maintenance solution from prototype to production
Designed and deployed data pipelines and ML algorithms under operational constraints
Led efforts on testing, CI, monitoring, and production feedback pipelines

Conducted health monitoring research on electromechanical flight-control actuators
Developed models combining signal processing, system modeling, and ML
Results included international patents and peer-reviewed publications

Education

https://theses.hal.science/tel-03934553
http://www.ampere-lab.fr/spip.php?article992

Skills

Python libs : Polars, Pandas, Skrub
Others : PostgreSQL, Azure Data Explorer

Kubernetes, Docker, Azure, Github, Terraform

Airflow

scikit-learn, MLFlow, Seldon, DVC

Grafana, Prometheus

Current : Python, SQL, Bash
In the past : Matlab, R, some Go

Mechatronics & Reliability