Martin Schmitz, CFDS

Martin Schmitz, CFDS has a diverse range of work experience in the financial industry. Martin is currently serving as a Senior Financial Engineer at FINVIA since August 2022. Prior to that, they worked at HQ Asset Management GmbH for nearly four years starting in October 2018. At HQ Asset Management, Martin held the position of Senior Researcher where they were responsible for various tasks including the development and implementation of a data pipeline, establishment of a data framework, creation and quality monitoring of data, and co-conception and implementation of an investment platform.

Before HQ Asset Management, Martin worked at Lingohr & Partner Asset Management for over a decade. During this time, they held various roles including Head of Quantitative Research from February 2015 to September 2018, Head of Research & Process Development from October 2012 to February 2015, and Portfolio Manager from July 2007 to October 2012.

Overall, Martin Schmitz, CFDS has a strong background in quantitative research, data analysis, and portfolio management.

Martin Schmitz, CFDS has a strong education history in the field of information technology and management. Martin obtained their Dipl.-Informatiker (FH) degree in Wirtschafts-Informatik from FOM University of Applied Sciences for Economics and Management, where they studied from 2001 to 2004. Prior to that, they completed their Fachinformatiker Systemintegration training at Sparkassen-Informatik-Systeme West from 1998 to 2001.

In addition to their formal education, Martin Schmitz has also pursued several certifications to enhance their skills and knowledge. Martin obtained a certification in Big Data Hadoop and Spark Developer from Simplilearn in January 2022. In December 2017, they obtained two certifications: Chartered Financial Data Scientist from DVFA e.V. and Neural Networks and Deep Learning from Coursera.

Overall, Martin Schmitz's education history reflects a strong background in information technology, management, and specialized training in big data, financial data science, and neural networks.