Daniel Lawrence
About Daniel Lawrence
Daniel Lawrence is a Data Scientist with a diverse background in finance and education, currently working at FINRA since 2021. He transitioned from quantitative trading to data science, leveraging his analytical skills and comprehensive education in mathematics, psychology, and finance.
Work at FINRA
Daniel Lawrence has been employed at FINRA as a Data Scientist since 2021. In this role, he applies his analytical skills to enhance data-driven decision-making processes within the organization. His experience in financial market analysis and data science contributes to FINRA's mission of protecting investors and ensuring market integrity.
Education and Expertise
Daniel Lawrence holds a Master of Science in Mathematical Finance from the University of Southern California, which he completed in 2010. He also earned a Bachelor of Arts degree in Mathematics and Psychology from the same institution in 2005. His educational background equips him with a comprehensive understanding of quantitative analysis, behavioral insights, and financial principles, which he integrates into his data science work.
Background in Data Science and Finance
Daniel transitioned from a career in quantitative trading to data science, leveraging his expertise in financial market analysis. He has worked in various capacities within the financial sector, including positions as a Quantitative Trader at Crabel Capital Management and Chopper Trading. His experience in these roles informs his current data science methodologies.
Previous Work Experience
Prior to his current role at FINRA, Daniel Lawrence held several positions in data science and analytics. He worked at Forbes Capital Solutions Inc. as a Data Scientist from 2018 to 2019 and at Glo from 2020 to 2021. Additionally, he served as a Data Analyst at Sense360 for two months in 2017. His diverse work history in both data science and quantitative trading enhances his analytical capabilities.
Teaching and Curriculum Development
Earlier in his career, Daniel Lawrence worked as a Senior Chess Instructor and Curriculum Designer at Academic Chess from 2002 to 2009. This role involved designing educational programs and teaching chess, which reflects his ability to communicate complex concepts effectively. His background in education complements his analytical skills in data science.