Data Analyst · Data Scientist · AI-assisted Automation
I build data projects that connect business questions, analytical thinking, machine learning and practical automation.
| Project | What it demonstrates | Status |
|---|---|---|
| Olist — E-commerce Analytics | Business & product analytics, e-commerce KPIs, customer/seller analysis | ✅ Live demo |
| ReverPro — International Comparative Analysis | International benchmarking, public-sector analysis, decision support | 🚧 Portfolio publication in progress |
| Graal — Predictive Modeling | Machine learning, predictive modeling, model evaluation | 🚧 Portfolio publication in progress |
| Trading Automation | Telegram signal processing, exchange API automation, risk controls, AI-assisted development | 🔒 Case study available; code under security review |
| TaxiFare — Big Data & ML Pipeline | Big Data workflow, cloud-oriented ML pipeline, model serving | 🚧 Portfolio publication in progress |
A business-oriented analytics project built around the Brazilian Olist e-commerce dataset. The portfolio version focuses on translating data into actionable business insights.
Live: https://ladyinet.fr/olist/
A real-world analytical case focused on an international comparative study within the ReverPro context. The portfolio version will present the methodology, comparative framework, findings and visual evidence without exposing confidential material.
A machine-learning project focused on the predictive modeling workflow: problem framing, preparation, experimentation, evaluation and interpretation.
An automation project connecting Telegram trading signals with structured validation, risk controls and Bybit order execution.
My role focused on requirements, workflow design, testing, validation and iterative project management, with implementation supported by AI coding tools.
A Le Wagon educational project used to demonstrate working with larger-scale data and an end-to-end machine-learning workflow, including cloud-oriented data processing and model serving.
- Business analytics: turning datasets into decisions and recommendations
- Data science: predictive modeling, evaluation and interpretation
- Data engineering mindset: reproducible pipelines and larger-scale data workflows
- Automation: APIs, Telegram workflows and external-system integration
- AI-assisted delivery: defining requirements, supervising implementation, testing and validating results
This profile is being actively structured for professional use. Selected projects are being published progressively with concise documentation, reproducible code where appropriate, and visual results.