Head of Quantitative Research & Structuring – Global Markets

About the RoleWe are seeking a skilled individual to run a team of quantitative researchers & structurers with at least 12 years of experience in researching and developing quantitative trading portfolios. The successful candidate will report to the Head of Global Markets and will also be a member of the Klay Investment Team.ResponsibilitiesResearch, develop and maintain quantitative methodologies for various portfolio solutions, including: (a) traditional style factor strategies on single stocks, (b) non-traditional thematic factor strategies, (c) non-linear risk factor strategies such as volatility, correlation, etc., (d) portfolio construction frameworks.Play a central role in portfolio design, construction, risk management, etc. to meet various return/risk objectives, not least in the context of broad "model portfolio" development.Source quantitative content from the sell-side, and filter/select strategies as appropriate to suite our investment objectives.Refine strategies with the sell-side as appropriate.Analyze large, multi-source financial datasets to identify trends and risk drivers.Work with trading and investment teams to generate data-driven insights and analytical tools.Collaborate with cross-functional teams to translate business requirements into scalable technical solutions.Ensure model accuracy through rigorous testing and ongoing performance monitoring.In the traditional investment banking model, you are a hybrid of a head/senior QIS structurer and a head/senior quantitative research analyst.QualificationsMaster’s degree in Financial Engineering, Mathematical Finance, Data Science, Statistics, or a related quantitative discipline.At least 12 years of professional experience finance in quantitative research, strategy or structuring capacity preferably, within asset management, banking, or investment firms.Strong proficiency in Python (Pandas, NumPy, Scikit-learn; experience with TensorFlow or PyTorch is a plus) and use of relevant AI tools, in particular deployment of ML techniques.