Jeff Gilmore
About Jeff Gilmore
Jeff Gilmore is a Model and Market Risk Analyst at Investec in London, United Kingdom, where he has worked since 2018. He possesses extensive experience in financial modeling and risk analysis, with a strong background in mathematics and cross-cultural communication.
Work at Investec
Jeff Gilmore has been employed at Investec since 2018, serving as a Model and Market Risk Analyst. He has accumulated six years of experience in this role while based in London, United Kingdom. His responsibilities include analyzing financial models and assessing market risks, utilizing advanced simulation techniques to inform decision-making processes.
Education and Expertise
Jeff Gilmore holds a Bachelor of Science in Mathematics from Trinity Western University, where he studied from 1997 to 2001. He furthered his education by obtaining a Master of Science in Applied Mathematics from Simon Fraser University, completing his studies from 2001 to 2003. His expertise encompasses object-oriented and parallel computing, which are essential for developing and managing complex financial models.
Professional Background
Before joining Investec, Jeff Gilmore held various positions in the financial sector. He worked as a Quantitative Analyst at Mizuho from 2014 to 2018 and as a Lead Financial Engineer at Markit from 2011 to 2013. His earlier experience includes a role as a Mathematician at QuIC Financial Technologies, Inc. from 2005 to 2010, and as a Research Assistant at Perceptronix Medical Inc. from 2003 to 2005.
Cross-Cultural Experience
Jeff Gilmore has significant experience working in diverse cultural environments, having worked in multiple countries including Canada, Norway, Germany, and the UK. This cross-cultural exposure enhances his ability to communicate effectively with clients and collaborate with colleagues from various backgrounds.
Technical Skills and Specializations
In his current role and previous positions, Jeff Gilmore specializes in equity (EQ) and foreign exchange (FX) model validation. He employs Monte Carlo simulation techniques to evaluate the impact of risk and uncertainty in financial forecasting models, ensuring the accuracy and reliability of pricing models in the financial sector.