Pegah Rahimian is a data scientist and researcher specialising in football intelligence, bringing together strong foundations in computer engineering, machine learning, and quantitative modelling with applied work in soccer analytics. Since November 2023, she has worked as a Soccer Analytics Researcher at Uppsala University, focusing on research-driven methods that help translate complex data into clearer insight for football decision-making. Alongside her academic work, she has also operated in an applied industry setting as a Football Data Scientist at Twelve Football(September 2023–November 2025), supporting football-facing analytics projects that align modelling, scouting, and performance insight with real-world constraints.
Her career includes roles beyond football that strengthen her technical depth in data and modelling. She worked as a Machine Learning Engineer at Bosch (January 2022–April 2022), and earlier as a Data Architect at Quomeda(November 2020–September 2021) and a Quantitative Model Developer at Nationale-Nederlanden (September 2019–January 2020). This blend of product, architecture, and modelling roles supports a practical skill set that is highly relevant to modern football analytics—where pipelines, model reliability, and usable outputs matter as much as the algorithm itself.
Academically, Rahimian completed a PhD in Computer Engineering (Informatics) at the Budapest University of Technology and Economics (2019–September 2023). She also holds an MSc in Business Information Technology from Corvinus University of Budapest (2017–2019), and a bachelor’s degree in Industrial Engineering from Alzahra University, giving her a strong multi-disciplinary base across systems thinking, data, and optimisation.
Since 2026, she has also contributed as an ISSPF Lecturer in Data Science & Soccer, bringing an applied, research-informed approach to teaching how analysts can build robust workflows—covering data preparation, modelling choices, validation, and the translation of findings into coach- and recruitment-friendly insights.
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