I'm a data scientist transitioning into MLOps engineering. Working with data has always felt like uncovering hidden structures beneath the surface — but today my curiosity goes further. I’m fascinated not only by insights, but by the systems that make models reliable, reproducible, and scalable. Building pipelines, automating workflows, orchestrating experiments, and turning prototypes into robust production solutions — this is where my detective work continues, now at the level of infrastructure.

My goal is to transition from data science to MLOps engineering by combining my background in statistical and stochastic modeling, optimization, and optimal control with modern engineering practices. I aim to build reliable, reproducible, and scalable machine learning systems that bridge the gap between experimentation and production.

• On Taxi Demand, Nutri Score
• Enhancing Client Retention, Sentiment Analysis
• Harvey