Enzo
Natali
Data meets strategy
for Insurance
Building ML models, AI agents and data pipelines that turn business complexity into clear decisions.
Where data meets strategy
I'm Enzo : a data scientist and business analyst completing a Joint MSc in Data & AI for Business at Mines Paris–PSL and Albert School. I bridge rigorous quantitative thinking with sharp business instinct.
Over three years I've delivered real projects for Generali, SNCF, LVMH, BNP Paribas, Edmond de Rothschild, Henkel, Carrefour and Asmodee : from predictive ML models and LLM-powered agents to Flask dashboards and end-to-end automations.
I believe the best data work is invisible: it becomes a product, a decision, or an automation that simply runs better.
Five highlights,
thirteen industries
6-month internship in Generali's Tech & Ops division. Built a Python script to automatically inventory all Power BI reports across the division (Streamlit MVP in one afternoon), deployed 15+ Power Automate workflows saving ~10 person-days/month, fully automated the REPERE training cycle end-to-end (HR ingestion → conditional emails → attendance tracking → PDF generation), rebuilt KPI dashboards with access controls and data validation, and delivered a production conversational AI agent (Copilot Studio) for project managers to query internal documentation in natural language.
Scraped and structured 2026 investment outlooks from 20+ major European asset managers (Amundi, BNP Paribas AM, Pictet, UBS AM, DWS, AXA IM…) managing >€10bn AUM. Built a full Flask web application : 10 pages including Executive Summary, Market Consensus, EdRAM Positioning, Commercial Angles, Peer Benchmark, and Geographic Lens : with interactive Plotly charts, URL-based filter sharing, and a premium ivory/gold design. Delivered to EdRAM's Head of Product Management for real sales strategy decisions.
Analyzed weather-to-incident correlation across SNCF's network. Engineered features (is_rush_hour, is_weekend, temperature delta, wind speed, season flags) and built a predictive model for daily default counts to enable proactive maintenance scheduling.
Ingested Board Game Arena's 1.92M player dataset, compressed to Parquet and queried with DuckDB for fast analytics. Segmented players into Casual / Regular / Hardcore clusters, built a bipartite player↔game graph and computed Jaccard similarity coefficients to map game affinity across the catalog.
Built a binary classifier for the French Ministry of Defense to detect AI-generated text vs. human-written content. Trained on a custom dataset with NLP preprocessing and fine-tuned classification architecture.
Where I've worked
Skills & tools
From raw data to production-grade AI agents : I cover the full stack of modern data science and analytics engineering.
Let's build
something great
Open to internships, full-time roles, freelance data projects, and interesting conversations about AI and data strategy.