Quantitative Strategy Lead (Investment Strategies, VP)

• As a team lead, you will oversee a team of quantitative professionals, driving research and analysis on a diverse range of external fund managers and investment strategies in delivering high-impact insights and tools that strengthen and inform the investment decision-making process.
• To develop the team’s expertise in leveraging data, quantitative techniques, and AI/ML methodologies to deliver comprehensive, data-driven insights and evidence-based recommendations.
• To enhance and innovate across various stages of the investment process by applying quantitative and AI/ML tools, thereby expanding our capabilities in scale, depth, and performance. Additionally, you will contribute to the broader organization by sharing analytical tools and data-driven insights, fostering collective domain expertise.

Responsibilities:

• Serve as a strategic thought partner to department and investment leaders, leveraging data, quantitative techniques, and AI/ML methods to enhance investment processes, shape the department’s analytical capabilities, and conduct research focused on capital allocations, portfolio construction, optimization, and risk management.
• Lead and manage quantitative research and analysis across the entire investment process, from manager screening and selection to strategy due diligence and portfolio construction, ensuring high research quality and timely delivery.
• Oversee and govern the implementation of models, analysis, and tools to ensure alignment with best practices and team-wide infrastructure, while managing the team's model and analytics operations.
• Facilitate the sharing and cross-pollination of quantitative applications, analysis, and tools across departments, while providing macro-level insights to the team.

Qualifications:

• Master’s or Doctoral degree in a quantitative field such as Mathematics, Statistics, Physics, Computer Science, Engineering, or Economics.
• 8-12 years of relevant experience in quantitative research and analysis, with a strong emphasis on applying quantitative methods and AI/ML techniques in capital allocation, portfolio construction, optimization, and risk management.
• Experience in a fund allocator environment is highly desirable, along with a solid understanding of macroeconomic trends, credit and equity investing, and capital markets, complemented by exceptional communication skills to effectively understand, influence, and secure buy-in from stakeholders.
• Proficiency in programming languages such as R or Python, along with SQL.