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 and develop 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.Apply machine learning techniques to generate predictive insights (starting with 3 specific models), which involves feature engineering, model tuning, and validation to ensure robustness and performance.Analyse large, multi-source financial datasets to identify trends and risk drivers.Support trading and investment teams with 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.QualificationsMaster’s degree in Financial Engineering, Mathematical Finance, Data Science, Statistics, or a related quantitative disciplineAt least 12 years of professional experience finance in quantitative research, strategy or structuring capacity preferably, within asset management, banking, or investment firmsStrong 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 techniquesRequired SkillsThough most of the work is expected to involve structured data sets, experience with unstructured data sets us a plusSolid understanding of the pros/cons of various machine learning techniques