Per Christian Nødtvedt
About Per Christian Nødtvedt
Per Christian Nødtvedt is an Analytics Manager at PRA Group in Oslo, Norway, where he applies machine learning and statistical methods to derive investment insights. He holds advanced degrees in Computer Science and Finance and has extensive experience in quantitative finance and data analysis.
Work at PRA Group
Per Christian Nødtvedt has served as Analytics Manager at PRA Group since 2017. In this role, he is responsible for applying machine learning and statistical methods to convert raw data into actionable investment insights. He manages a team that specializes in merging and comparing raw data sets with current portfolios, which aids in creating nuanced investment cases. His work focuses on enhancing analytical problem-solving techniques and improving the communication of findings within the team.
Education and Expertise
Per Christian Nødtvedt holds a Master of Science in Computer Science from the Norwegian University of Science and Technology (NTNU), which he completed from 1997 to 2002. He also studied at the National School of Computer Science and Applied Mathematics of Grenoble, where he completed an exchange year in Computer Science from 2000 to 2001. Additionally, he earned a Master in Finance from the London Business School in 2009. His educational background supports his expertise in applied machine learning and quantitative finance.
Previous Work Experience
Before joining PRA Group, Per Christian Nødtvedt worked at Norges Bank Investment Management as a Senior Analyst from 2010 to 2014. He also held the position of Senior Solutions Architect at bWise AS from 2014 to 2017. Earlier in his career, he served as a Senior Consultant at Det Norske Veritas from 2002 to 2010. His diverse experience in these roles has contributed to his analytical skills and understanding of financial markets.
Technical Skills
Per Christian Nødtvedt utilizes R-programming for business intelligence and visual analytics, particularly in debt portfolio valuations. His application of machine learning and statistical methods allows him to effectively analyze data and derive insights that inform investment strategies. He works at the intersection of applied machine learning and quantitative finance, enhancing the analytical capabilities of his team.