Marcos Del Cueto
Senior Data Scientist
Greater Liverpool Area United Kingdom
Summary
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Senior🎓
Top SchoolMarcos Del Cueto is a Senior Data Scientist based in the Greater Liverpool Area with six years of experience turning complex data into predictive ML solutions for ad-tech. With a PhD in Theoretical Chemistry and a research background in computational modelling and materials discovery, he brings rigorous scientific methods and high-performance computing expertise to production problems. At Dataseat (now part of Verve Group) he focuses on frameworks that reveal user behaviour and drive impactful, data-driven decisions. He has a track record of publishing peer-reviewed research, supervising students, and building bespoke databases and visualization pipelines from his academic work. Comfortable with Python, SQL, cloud and even Fortran, Marcos combines research-grade modelling skills with pragmatic engineering to bridge discovery and deployment. He also writes about science and programming, making technical topics accessible beyond specialist audiences.
6 years of coding experience
6 years of employment as a software developer
Doctor of Philosophy - PhD, Theoretical Chemistry and Computational Modelling, Doctor of Philosophy - PhD, Theoretical Chemistry and Computational Modelling at Universidad Autónoma de MadridBachelor's degree - Erasmus International Mobility Program, Chemistry, Bachelor's degree - Erasmus International Mobility Program, Chemistry at University of Leeds
Spanish, English, German
Github Skills (4)
photovoltaic9
prediction8
molecule8
machine-learning7
Programming languages (1)
Github contributions (5)
marcosdelcueto/LaLigaPredictor
Feb 2020 - Dec 2020
Contributions:52 commits, 48 pushes, 1 branch in 9 months
marcosdelcueto/NonFullereneAcceptorPrediction
Jun 2020 - Nov 2021
Code uses different machine learning methods to predict the power conversion efficiency of organic photovoltaic devices with non-fullerene acceptors
Contributions:78 commits, 9 pushes in 1 year 5 months
machine-learningmaterials-sciencemoleculephotovoltaicprediction
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