Bernhard Pfann, Cfa
About Bernhard Pfann, Cfa
Bernhard Pfann is a Data Scientist specializing in Advanced Analytics at Raiffeisen Bank International AG. He holds a CFA charter and has a strong background in finance and analytics, with experience at various financial institutions in Austria.
Work at Raiffeisen Bank International
Bernhard Pfann currently serves as a Data Scientist in Advanced Analytics at Raiffeisen Bank International AG. He has held this position since 2021, contributing to the bank's data-driven decision-making processes. His role involves utilizing advanced analytics techniques to extract insights from data, which supports various business applications within the bank.
Education and Expertise
Bernhard Pfann holds multiple degrees in finance and analytics. He earned a Master of Science (M.Sc.) in Business Analytics from ESADE Business & Law School in 2020. Prior to that, he completed a Master of Science (M.Sc.) in Finance & Accounting at WU (Wirtschaftsuniversität Wien) in 2015. He also holds a Bachelor of Science (B.Sc.) in Economics and Business Administration from WU, obtained in 2013. Additionally, he is a CFA charterholder, reflecting his expertise in finance and investment management.
Professional Background
Bernhard Pfann has a diverse professional background in finance and analytics. He worked at Vienna Insurance Group AG as a Risk Manager in Asset Risk Management for eight months in 2020. Prior to that, he was with PwC Austria, where he served as an Associate/Trainee in FS Deals from 2016 to 2018 and later as a Senior Associate in FS Valuation & Strategy from 2018 to 2020. His early career included internships at UniCredit Bank Austria AG and Allianz Investmentbank AG, where he gained experience in commercial real estate and institutional sales.
Technical Skills
Bernhard Pfann possesses strong technical skills in data science and analytics. He is fluent in Python, which he utilizes for various personal machine learning and analytics projects. His ability to combine technical data insights with business applications enables him to effectively bridge the gap between data science and practical business needs.