Summary
Laura Zingaretti is a Senior Statistician with a PhD in Quantitative Genetics and over a decade of experience applying machine learning, deep learning, and advanced statistical methods across biotech and agrigenomics. She has driven data-driven decision making at organizations including AstraZeneca, Roche, BASF and CRAG, blending Bayesian and mixed-model expertise with practical tools like BGLR, ASReml and deep neural networks. Her work spans from genomic prediction and polyploid simulation tools to automated image-analysis pipelines for livestock and fruit phenotypes—where she pioneered generative models to simulate new genotypes and phenotypes. Comfortable in R, Python, Linux and Azure DevOps, she also integrates LLMs for retrieval-augmented generation, fine-tuning and automated text analysis to enhance research workflows. A former assistant professor and consultant for government agriculture departments, she combines strong communication skills with reproducible open-source software (see her GitHub) to preserve and operationalize data legacy. Based in Barcelona, she brings a rare mix of academic rigor, production-ready engineering and domain expertise in biostatistics and bioinformatics.
10 years of coding experience
16 years of employment as a software developer
Doctor of Philosophy - PhD, Genética, Doctor of Philosophy - PhD, Genética at Universitat Autònoma de Barcelona
Maestría en Estadística Aplicada, Estadística, Maestría en Estadística Aplicada, Estadística at Universidad Nacional de Córdoba
Prof. en Matemática, Estadística, Prof. en Matemática, Estadística at Universidad Nacional de Villa María
English, Spanish