Qianyue Zhang
About Qianyue Zhang
Qianyue Zhang is a Quantitative Researcher currently employed at Squarepoint Capital in London. She has a diverse background, including roles at Jaguar Land Rover, Goldman Sachs, UNCTAD, and the University of Cambridge, where she also completed her PhD in Process System Engineering.
Work at Squarepoint Capital
Qianyue Zhang has been employed at Squarepoint Capital as a Quantitative Researcher since 2022. In this role, Zhang applies quantitative analysis and research methodologies to support the firm's investment strategies. Squarepoint Capital is known for its focus on data-driven trading and investment solutions, and Zhang's expertise contributes to the firm's analytical capabilities.
Previous Experience in Engineering and Finance
Prior to joining Squarepoint Capital, Qianyue Zhang held several positions that provided a diverse range of experiences. Zhang worked as an Advanced Manufacturing Engineer at Jaguar Land Rover for three months in 2017, where responsibilities included optimizing manufacturing processes. In 2021, Zhang served as a Summer Associate at Goldman Sachs for two months, gaining insights into financial markets and investment strategies. Additionally, Zhang worked as a Data Scientist at UNCTAD in 2020 for three months, focusing on data analysis and research.
Teaching Experience at University of Cambridge
From 2018 to 2022, Qianyue Zhang was a Teaching Assistant at the University of Cambridge. During this four-year tenure, Zhang supported academic programs by assisting in the instruction of courses related to engineering and quantitative methods. This role involved engaging with students and contributing to their learning experiences.
Education and Expertise
Qianyue Zhang completed a Doctor of Philosophy (PhD) in Process System Engineering at the University of Cambridge from 2018 to 2022. Prior to this, Zhang earned a Master's degree in Chemical Engineering from Imperial College London, studying from 2014 to 2018. This educational background provides a strong foundation in quantitative research and engineering principles.