Pablo Fahnle
About Pablo Fahnle
Pablo Fahnle serves as the Head of Data Extraction at Lustre, where he leads a globally distributed team in building data pipelines. He has extensive experience in data science, machine learning, and programming, with a background that includes roles at Google, Microsoft, and various educational institutions in Buenos Aires.
Current Role at Lustre
Pablo Fahnle serves as the Head of Data Extraction at Lustre since 2021. In this role, he leads a globally distributed team focused on building both manual and automatic data pipelines. His responsibilities include integrating data into Lustre's Knowledge Graph and Neural Net, utilizing his expertise in machine learning and programming heuristics to enhance data ingestion processes.
Previous Experience in Data Science and Engineering
Prior to his current position, Pablo held several significant roles in data science and engineering. He worked at iProspect as Head of Data Science & Machine Learning from 2018 to 2020. He also served as Head of Data Driven Business at Albor for 11 months in 2020. His earlier experience includes positions at Google as an AdWords Associate and AdSense Associate, as well as at Voölks as VP & Product Engineering Director.
Academic Background and Specializations
Pablo Fahnle has a robust academic background. He earned a Master's Degree in Business Administration (MBA) from Instituto Tecnológico de Buenos Aires from 2016 to 2017. He also holds a Bachelor's Degree in Business Administration and Computer Science from the same institution, completed from 2006 to 2009. Additionally, he studied at Digital House Argentina, achieving specializations in Data Science, Deep Learning & AI, and AWS Cloud Architecture.
Teaching Experience at ITBA
Pablo has a history of teaching at Instituto Tecnológico de Buenos Aires (ITBA). He worked as a Professor and Researcher from 2014 to 2018 and previously served as a Professor Assistant in Advanced Data Management in 2009. His tenure at ITBA spanned several years, contributing to the education of students in data-driven fields.
Notable Academic Achievement
Pablo scored a perfect 10/10 while defending his Master's Thesis titled 'Applying Machine Learning to Crypto-Market'. This achievement highlights his proficiency in machine learning and its application in financial markets, showcasing his analytical skills and expertise in the field.