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Join us at Barclays as a Quantitative Analyst dedicated specifically to the Equity Derivatives within our Global QA Equity and Hybrid Products team. The QA Equity & Hybrid Products team is part of the Global Quantitative Analytics group (QA) and is responsible for research, development and implementation of quantitative models for the equity derivatives business. It covers equity flow products, equity structured & hybrid products, quantitative index and strategies business and strategic/corporate derivatives business. The team works closely with the trading desk to provide cutting-edge valuation and risk management solutions, and with IT to build strategic technology platforms for the bank. The Equity & Hybrid Products team has members in London, Hong Kong, New York, Paris and Prague and works as a closely integrated team, sharing expertise and responsibility for key deliverables.
Job Responsibility
Development and implementation of quantitative models and strategies to derive insight into market trends and optimize trading decisions, pricing, and risk management across various financial products and markets
Working closely with sales teams to identify clients' needs and develop customised solutions
In-depth research, data analysis, and statistical modelling to derive insights into market trends, pricing, and risk dynamics
Provide front office infrastructure support though ownership and maintenance of analytical libraries
Provision of expertise on quantitative methodologies, technological advancements, and industry best practices to drive innovation within the trading environment
Requirements
A master's degree or equivalent in Financial/Applied Mathematics, Physics, Engineering
Good knowledge about probability theory, calculus, stochastic process
Strong technical skills in C++ and Python
Ability to work independently and manage multiple projects in a fast-paced environment
Ability to explain complex ideas in a clear and coherent manner
Nice to have
Experience in quantitative analysis of equity derivatives
Knowledge about numerical methods of PDE and Monte Carlo