About me
I trained as an astrophysicist — spending several years modeling relativistic jets, fitting models to multi-wavelength observational data, and running large simulations on European supercomputing clusters. The work required building tools from scratch, reasoning carefully about sources of error, and producing results that could be reproduced and checked.
I'm now looking to apply that same approach in data science and engineering — places where physical intuition and methodological rigor are genuinely useful, not just listed on a CV.
I'm open to junior / early-career roles in data science and engineering.
Research, in one minute
I study how energetic outflows from extreme objects shape galaxies. Think of it as "weather models" for cosmic jets: we simulate, compare with real observations, and keep only what survives the data.
The same logic applies across domains — build a model, test it against real measurements, revise it, and be clear about what you can and cannot conclude.
Featured project
A quick highlight from my portfolio.
More projects are available on the Projects page.
I'm open to early-career roles in data science or data engineering — let's connect.