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I am Leonardo Cristella, a data manager and scientific Python software engineer based in Geneva, with a PhD in Physics from the University of Bari. I specialize in building scalable data pipelines, HPC workflows, and ML-driven analysis for neuroscience, astronomy, and Earth observation. I thrive in collaborative environments, bridging research and engineering, and I am passionate about data governance, FAIR principles, and making complex scientific workflows reliable and reproducible.…I am Leonardo Cristella, a data manager and scientific Python software engineer based in Geneva, with a PhD in Physics from the University of Bari. I specialize in building scalable data pipelines, HPC workflows, and ML-driven analysis for neuroscience, astronomy, and Earth observation. I thrive in collaborative environments, bridging research and engineering, and I am passionate about data governance, FAIR principles, and making complex scientific workflows reliable and reproducible.

Leonardo Cristella

Data Scientist, Data Analyst, Full Stack Developer, +2

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I am Leonardo Cristella, a data manager and scientific Python software engineer based in Geneva, with a PhD in Physics from the University of Bari. I specialize in building scalable data pipelines, HPC workflows, and ML-driven analysis for neuroscience, astronomy, and Earth observation. I thrive in collaborative environments, bridging research and engineering, and I am passionate about data governance, FAIR principles, and making complex scientific workflows reliable and reproducible.…I am Leonardo Cristella, a data manager and scientific Python software engineer based in Geneva, with a PhD in Physics from the University of Bari. I specialize in building scalable data pipelines, HPC workflows, and ML-driven analysis for neuroscience, astronomy, and Earth observation. I thrive in collaborative environments, bridging research and engineering, and I am passionate about data governance, FAIR principles, and making complex scientific workflows reliable and reproducible.

Available to hire

I am Leonardo Cristella, a data manager and scientific Python software engineer based in Geneva, with a PhD in Physics from the University of Bari. I specialize in building scalable data pipelines, HPC workflows, and ML-driven analysis for neuroscience, astronomy, and Earth observation.

I thrive in collaborative environments, bridging research and engineering, and I am passionate about data governance, FAIR principles, and making complex scientific workflows reliable and reproducible.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Intermediate

Intermediate

Intermediate

Intermediate

Intermediate

Intermediate

Language

English

Fluent

French

Fluent

Italian

Fluent

Work Experience

Scientific Python Software Engineer at EPFL (École Polytechnique Fédérale de Lausanne)

July 1, 2022 - December 1, 2024

Developed a Snakemake workflow to automate generation and processing of neuroscience simulations within the Blue Brain Project, and registered results in a Knowledge Graph (Blue Brain Nexus) via APIs: the Blue Brain Atlas Pipeline (BBAP). The BBAP manages dependencies among heterogeneous datasets (brain region hierarchy and meshes, volumetric NRRD files) and triggers pipelines leveraging deep learning techniques for image pattern recognition and text mining. Ensured data governance and FAIR compliance through APIs enforcing ontologies and data schemas; daily use by 50+ life-scientists.

Data Analyst at CERN

January 1, 2020 - March 1, 2022

Reconstruction framework of a High Granularity Calorimeter (HGCal) upgrade for the CMS experiment. Implemented clustering algorithms based on 2D energy density and 3D spatial propagation (TICL). Developed a CNN for particle identification and energy regression on 3D TICL clusters (classification accuracy > 95%). Designed a validation suite API for continuous development of HGCAL pattern recognition via Monte Carlo simulations. Algorithms leverage GPUs/FPGA with CUDA; led a team of two data scientists.

Data Analyst at Planetek Italia - University of Bari (Physics Department)

January 1, 2019 - December 1, 2019

Scientific computing of satellite data for Earth observation within the CLOSE To The Earth European project, leveraging Machine Learning and Big Data algorithms optimized for GPU on HPC data centers. Developed a Convolutional Neural Network to process and classify 3D Remote Sensing images. Application on the EuroSAT dataset achieves classification accuracy greater than 90%, trained with TensorFlow.