Jan Stribny has a diverse work experience starting from 2008. Jan worked as a part-time Developer at Web Progress from May 2008 to August 2009. Jan then worked as a Visiting Business Analyst at Accenture from August 2012 to November 2012. Following that, they did an Internship at Coober fund from June 2013 to August 2013. Jan gained experience as a Structured Credit Analyst at Dynamic Credit from February 2014 to December 2015. Jan then joined Deep Blue Capital N.V. as a Senior Quantitative Trader in January 2016 and worked there until March 2021. Currently, they are working as an Associate Director Credit & Insurance Linked Investments at PGGM since April 2021.
Jan Stribny has a Master's Degree in Quantitative Risk Management from the Duisenberg School of Finance, which they obtained from 2013 to 2014. Jan also has a Bachelor's Degree in Quantitative Economics from the Institute of Economic Studies, Faculty of Social Sciences, Charles University, where they studied from 2010 to 2013.
In addition to their formal education, Jan Stribny has obtained several certifications. Jan is a Chartered Financial Analyst (CFA), which they received from the CFA Institute in November 2022. Jan has also completed various online courses on Coursera, earning certifications such as "Advanced Portfolio Construction and Analysis with Python" from Coursera in May 2020, "Introduction to Portfolio Construction and Analysis with Python" from EDHEC Business School on Coursera in January 2020, "Structuring Machine Learning Projects" in April 2018, "Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization" in February 2018, "Neural Networks and Deep Learning" in December 2017, "Machine Learning" by Stanford University on Coursera in March 2017, "Machine Learning specialization" by the University of Washington on Coursera in February 2017, "Machine Learning: Clustering & Retrieval" and "Machine Learning: Classification" by the University of Washington on Coursera in October 2016 and September 2016 respectively, "Machine Learning Foundations: A Case Study Approach" by the University of Washington on Coursera in August 2016, and "Machine Learning: Regression" by the University of Washington on Coursera in August 2016.
Jan Stribny has also completed two courses on edX, namely "Introduction to Computational Thinking and Data Science" by MITx in June 2015 and "The Analytics Edge" by MITx in June 2015.
Org chart
This person is not in the org chart
Teams
This person is not in any teams
Offices
PGGM
2 followers
Wij zijn PGGM. Een vaste waarde in zorg en welzijn. We zetten ons in voor een betaalbaar, goed en duurzaam pensioen voor pensioenfondsen - onze opdrachtgevers - en hun deelnemers. Daarnaast dragen we bij aan een leefbare wereld, gezond werken en vitaal ouder worden. We zijn een coöperatieve pensioenuitvoeringsorganisatie zonder winstoogmerk. We bieden onze klanten pensioenbeheer, vermogensbeheer en beleidsadvisering. PGGM beheert de pensioenen van verschillende pensioenfondsen, de daarbij aangesloten werkgevers en hun werknemers. Op 31 december 2020 beheerden we 266 miljard euro aan pensioenvermogen en administreerden pensioen voor 4,4 miljoen deelnemers. Daarnaast ontwikkelen wij aanvullende diensten voor de ruim 750.000 mensen afkomstig uit de sector zorg en welzijn die zich aan onze ledenorganisatie PGGM&CO hebben verbonden. ----- We are PGGM. A solid value in health and welfare. We work for good, affordable and sustainable pensions for pension funds - our clients - and their participants. We also contribute towards a liveable world, occupational health and retaining vitality in old age. PGGM is a not-for-profit cooperative pension fund service provider. We provide services in the field of pension management, integral asset management and executive advice to pension funds, affiliated employers and their employees. On 31 December 2020, we managed pension assets worth EUR 266 billion for different pension funds. In addition, we are developing supplementary services in the fields of pensions, healthcare, living and working for employers and the more than 750,000 members we have in the health and social sector.
