Ben Stemper
About Ben Stemper
Ben Stemper is a Quantitative Researcher at Squarepoint Capital in London, specializing in quantitative finance and machine learning techniques. He has a strong academic background with degrees from the University of Oxford, Technische Universität Berlin, and The London School of Economics.
Work at Squarepoint Capital
Ben Stemper has been employed at Squarepoint Capital as a Quantitative Researcher since 2023. He works on-site in London, England. In this role, he focuses on developing advanced option pricing and risk analytics tools specifically for fixed income options. His expertise in quantitative finance is supported by his proficiency in various Python libraries, which he utilizes to enhance the firm's analytical capabilities.
Previous Experience at Citadel
Before joining Squarepoint Capital, Ben Stemper worked at Citadel as a Quantitative Researcher from 2019 to 2022. During his three-year tenure in London, he contributed to quantitative finance projects, leveraging his analytical skills and knowledge in the field. His experience at Citadel helped him develop a strong foundation in quantitative research methodologies.
Entrepreneurial Role at Grey Swan Finance
Ben Stemper co-founded Grey Swan Finance, where he served as CEO from 2022 to 2023. His leadership in this hybrid role in London involved overseeing the company's operations and strategic direction. This experience provided him with insights into the intersection of quantitative finance and entrepreneurship.
Education and Expertise
Ben Stemper holds a Master's Degree in Mathematics from the University of Oxford, which he completed from 2012 to 2013. He furthered his education by obtaining a Doctor of Philosophy (Ph.D.) in Mathematical Finance & Machine Learning from Technische Universität Berlin between 2015 and 2018. Additionally, he earned a Bachelor's Degree in Mathematics from The London School of Economics and Political Science (LSE) from 2009 to 2012. His academic background supports his application of machine learning techniques in quantitative finance.