Veerle Dinghs
About Veerle Dinghs
Veerle Dinghs is a Risk Analyst at RiskQuest in Amsterdam, with a background in data science and risk modeling, and holds a Master of Science in Mathematics from VU Amsterdam.
Professional Background in Risk Analysis
Veerle Dinghs serves as a Risk Analyst at RiskQuest in Amsterdam, Noord-Holland, Nederland. She joined the company in August 2021, bringing her expertise in data science and risk modeling. Prior to this, she spent 11 months at de Volksbank as a Werkstudent Data Science & Risk Modelling. During this time, she honed her skills in analyzing and mitigating risk factors, a crucial aspect of her current role at RiskQuest.
Educational Background in Mathematics
Veerle Dinghs completed her advanced education at VU Amsterdam, earning a Master of Science in Mathematics from 2020 to 2022. Prior to this, she graduated with a Bachelor of Science in Wiskunde from Vrije Universiteit Amsterdam, where she studied from 2016 to 2019. Her academic journey provided her with a solid foundation in quantitative analysis, statistical methods, and mathematical modeling, all of which are integral to her current role in risk analysis.
Previous Experience in Data Science and Risk Modelling
Before joining RiskQuest, Veerle Dinghs accumulated significant experience in data science and risk modeling. She worked at de Volksbank for 11 months as a Werkstudent Data Science & Risk Modelling, where she contributed to various projects focused on data-driven risk management. Additionally, she interned at Colourful Rebel B.V. from 2019 to 2020 for 7 months, gaining practical experience in data analysis and operational research.
Extensive Experience in the Service Industry
Veerle Dinghs worked at Boscafé Het Maasdal for 7 years, from 2013 to 2020, in the service industry. This long tenure allowed her to develop essential soft skills, such as effective communication, customer service, and teamwork. These skills complement her technical expertise in data science and risk analysis, providing a well-rounded professional profile.