Head of Quantitative Research & Structuring – Global Markets

Klay; Dubai; On-site; AED 600,000 - 1,000,000; Full time

Job summary

Klay is seeking a seasoned quantitative researcher/structurer to lead a team for developing advanced trading portfolios. The role reports to the Head of Global Markets and sits within the Klay Investment Team, focusing on research, design, and risk management of model portfolios.

The ideal candidate has 12+ years in quantitative roles, excels in Python and ML, and collaborates across cross-functional teams to translate business needs into scalable solutions.

Qualifications

• Master’s degree in Financial Engineering, Mathematical Finance, Data Science, Statistics, or related quantitative field.
• At least 12 years of professional experience in quantitative research, strategy, or structuring, preferably within asset management, banking, or investment firms.
• Strong proficiency in Python and ML tools; experience with TensorFlow or PyTorch is a plus.

Responsibilities

• Research and develop maintain quantitative methodologies for portfolio solutions and strategies.
• Lead portfolio design, construction, and risk management to meet return/risk objectives.
• Source quantitative content from the sell-side and filter/select strategies for investment objectives.

Skills

Quantitative research

Python programming

Machine learning

Data analysis

Education

Master’s degree in Financial Engineering or related

Tools

Python (Pandas, NumPy, Scikit-learn)

TensorFlow

PyTorch

Job description

About the Role

We 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.

Responsibilities

• Research 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.

Qualifications

• Master’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

Required Skills

• Though most of the work is expected to involve structured data sets, experience with unstructured data sets us a plus
• Solid understanding of the pros/cons of various machine learning techniques