Experienced Data Scientist and ML Engineer with a strong background in statistics. This page traces my path: employment, freelance and volunteer work, education and certifications.
Experience#
Employed
Dec 2023 – present3 yrs
Data Scientist & ML Engineer
Orange · Rennes, FR Current
I design and productionise the forecasting models that help Orange anticipate its technicians' workload across the whole French telecom network, and I explore AI agents to automate back-office work.
- Nationwide forecasting: time-series models that anticipate technician activity across every network domain, to size teams ahead of demand.
- Generative AI (R&D): feasibility study of AI-agent and LLM architectures to automate business processes and speed up decision-making.
- MLOps on GCP: automated ingestion, preprocessing and training pipelines, with versioning, performance monitoring and continuous deployment.
- Data Science lead: technical reference for the team, mentoring interns and apprentices.
- Python
- Time series
- LLM & agents
- GCP
- Airflow
- CI/CD
- GitLab
- Docker
- Linux
Jan 2021 – Nov 20232 yrs 11 mo
Data Scientist & MLOps
BPCE-SI · Aix-en-Provence, FR
At the heart of BPCE group's IT, I took marketing use cases from prototype to production, and helped build the tooling that standardises that path for the whole group.
- Customer insight: advanced segmentation, churn prediction, sentiment analysis of customer feedback and a recommendation engine.
- Life events: models predicting key moments of the customer journey (retirement, first job, moving house), deployed to production to offer the right advice at the right time.
- Generative AI: implemented evaluation methods for a RAG-based conversational agent, to measure how relevant and reliable its answers are.
- In-house MLOps library: contributed to the Python tool that automates and standardises how the group packages and deploys its machine-learning models.
- Industrialisation and cloud: CI/CD, production monitoring and migration of use cases to GCP (BigQuery, Vertex AI), hand in hand with Data Engineers and DataOps.
- Agile delivery: Scrum projects with regular demos to stakeholders.
- Python
- PySpark
- Time series
- LLM
- BigQuery
- Vertex AI
- Airflow
- MLOps
- CI/CD
- NLP
- Scrum
- Linux
Jul 2019 – Dec 20201 yr 6 mo
Data Scientist & IoT Analytics
Traxens · Marseille, FR
At this smart-container specialist, I turned IoT sensor data into decision tools for shipping logistics: maintenance, cold chain and security.
- Predictive maintenance: estimated the remaining battery life of tracking devices, to plan interventions and manage the fleet's life cycle.
- Cold chain: temperature forecasting for refrigerated containers, at the core of the alerting systems.
- Security: door-opening detection algorithms built on sensor signals.
- Computer vision: processed and classified satellite imagery to annotate areas relevant to maritime transport.
- Data quality: governance, monitoring and hardening of the analytics and modelling pipelines.
- Research collaboration: worked with a research lab on automated cleaning of sensor data using reinforcement learning.
- Data Science and statistics lead: methodological reference for the team on modelling and data analysis.
- Python
- IoT
- Time series
- Computer vision
- Predictive maintenance
- Reinforcement learning
- Linux
Oct 2017 – Jun 20191 yr 9 mo
Data Scientist
IT-CE · Marseille, FR
For the Caisse d'Épargne banks' IT, I put machine learning to work for lending, against fraud, for customer retention and for automated document processing.
- Fraud prevention: unsupervised anomaly-detection model to flag fraudulent cheques and better protect customers.
- Credit scoring: built a scoring model to set the interest rate of the loans offered to customers.
- Life-insurance beneficiaries: automatically identified beneficiaries from heterogeneous documents (PDFs, scanned handwritten letters, free-text notes), combining named-entity recognition (NER) and computer vision.
- Retention: churn models for young customers, professionals and businesses, the basis of targeted loyalty strategies.
- Data lake: ingested and structured data with PySpark to centralise analysis and model deployment.
- Decision support: interactive dashboards turning model outputs into action plans for business teams.
- Python
- PySpark
- SQL
- NER
- Computer vision
- Credit scoring
- Anomaly detection
- Churn
- Data Lake
- Linux
Sep 2014 – Sep 20173 yrs 1 mo
Biostatistician
INSERM · Paris, FR
In medical research, I applied statistics to genomics and epidemiology: that is where my methodological rigour was forged.
- Genomics: inferential analyses and clustering of genomic profiles to reveal biologically meaningful patient subgroups.
- High-dimensional data: selected candidate genes among thousands of variables with penalised regression (lasso) and false discovery rate (FDR) control.
- Oncology: modelled the clinical impact of biomarkers on leukaemia progression with multivariate methods.
- Survival analysis: Cox models and Kaplan-Meier curves to assess the prognostic value of these biomarkers on progression-free survival.
- Epidemiology: dengue spread model combining biological and environmental factors to simulate outbreak dynamics.
- Causal inference: estimated the effect of vector-control actions on dengue incidence, making confounders explicit with causal graphs (DAGs) and adjusting for them with propensity-score weighting.
- R
- Multivariate statistics
- Causal inference
- Survival analysis
- Genomics
- Epidemiology
- Modelling
- Linux
Freelance
Aug 2021 – present5 yrs 4 mo
Mentor & Instructor
OpenClassrooms · Remote Ongoing
I mentor and train learners on the Data Scientist Machine Learning, Data Engineer and AI Engineer paths, project by project, from scoping to deployment.
- 18 learners mentored and trained throughout their path, one to one.
- Machine learning and MLOps: text and image classification, serving models behind an API, experiment tracking and CI/CD pipelines.
- Generative AI: RAG systems, LLM evaluation, AI agents and fine-tuning.
- Data engineering: SQL and NoSQL databases, ingestion and orchestration pipelines, cloud infrastructure.
- Python
- SQL
- scikit-learn
- MLflow
- FastAPI
- Streamlit
- LangChain
- PySpark
- Kafka
- Kestra
- CI/CD
- Teaching
Volunteer
Feb 2019 – Jun 20195 mo
Data Scientist
With this volunteer collective, I worked on the Open Food Facts project, the open database of food products: statistical analysis, Nutri-Score prediction and data quality.
- Statistical analysis and report on the data of the Open Food Facts project.
- Nutri-Score prediction: a model that estimates it for products that don't have one yet.
- Data quality: an audit of the database, then improvements to its quality.
- Python
- pandas
- NumPy
- scikit-learn
- statsmodels
- FastAPI
- Data quality
Education#
| Organization | Degree | |
|---|---|---|
| Aix-Marseille Université Marseille, FR | Master's in Biostatistics | |
| Université des Antilles Martinique, FR | Bachelor's in Mathematics, Computer Science and Biology |
Certifications#
A certification doesn’t replace production experience, but it strengthens its foundations. It’s also my way of keeping on learning, and of checking that what I know holds up under a demanding exam.
 | Professional Machine Learning Engineer Google Cloud2025 – 2027 | 2025 – 2027 |
|---|
Skills#
| Category | Skills / technologies |
|---|---|
| Languages | Python, R, SQL, Bash, Rust, TypeScript |
| Time series | forecasting, pricing, pattern mining, anomaly detection |
| Statistics | causal inference, A/B testing, Bayesian inference, survival analysis, multivariate statistics, penalised regression |
| Machine learning | gradient boosting, deep learning, clustering, recommender systems, computer vision, reinforcement learning |
| Generative AI | LLM, AI agents, RAG |
| Frameworks & tools | scikit-learn, PyTorch, statsmodels, LangGraph, MLflow, dbt, Airflow, PySpark, BigQuery |
| Cloud & MLOps | GCP, Vertex AI, Docker, Terraform, Git, CI/CD, Linux |
