Stavroula Diamantopoulou

Data Science & Machine Learning Lead at FINARTIX Fintech Solutions S.A.

Stavroula (Renia) Diamantopoulou has a diverse work experience, starting with their current role as the Data Science & Machine Learning Lead at FINARTIX Fintech Solutions S.A. Since 2023, they have been leading the data science and machine learning efforts at the company. Prior to this, from 2019 to 2023, they worked as a Senior Data Scientist at Aviva where they played a crucial role in developing the Customer Engagement Engine using AI targeting and personalized messaging. Stavroula (Renia) closely collaborated with the marketing team to find potential customers and run campaigns.

Before Aviva, from 2017 to 2019, Stavroula worked as a Data Scientist at Lendable, and from 2015 to 2017, they held a position as a Data Scientist/Analyst at What Now Travel. Stavroula (Renia)'swork experience also includes a research role at University College London from 2012 to 2016 and student projects/courses at Universiteit Utrecht from 2010 to 2012. Stavroula (Renia) also has experience with master's dissertation thesis work at Leiden University and a 2-month project at SRON Netherlands Institute for Space Research.

Additionally, Stavroula has undertaken a subsidized program in Mechanics provided by AKTOR SA in 2009 and gained practical experience through a practice role at State General Hospital "G. Gennimatas" in 2006.

Stavroula (Renia) Diamantopoulou has a diverse education history. Stavroula (Renia) obtained a Diploma in Applied Mathematics and Physics from the National Technical University of Athens in 2009. Stavroula (Renia) then pursued further studies in Astronomy and Astrophysics, earning a Master of Science (MS) degree from Utrecht University in 2012. Additionally, they completed a Master's degree in Astrophysics from Leiden University in the same year. Stavroula also has a Research (MPhill) degree in Physics and Astronomy from UCL, which they obtained in 2016. Furthermore, they have completed several certification courses in areas such as Getting and Cleaning Data, R Programming, and The Data Scientist's Toolbox from Coursera.

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