Senior Quant Analyst/Strategist - Global Asset Manager

My client is a global, multi-strategy, multi-billion-dollar investment firm currently seeking a Senior Quant researcher to join their Singapore arm to work closely with a team of Equity Portfolio Managers. Demonstrating acute capabilities with equity investment strategies including risk, performance attribution, and investment processes, you must also be able to demonstrate expertise in quantitative analysis techniques. This should include statistical analysis, and an ability to programmatically work with market data and large data sets.

Responsibilities

• Working directly with PMs to drive performance and scale with the aim of improving profitability of the equity business.
• Authenticate new research and develop ideas that can be delivered to Portfolio Managers, through concise quantitative analysis
• Through precise and specific use of data provide analysis on how to best improve risk-adjusted returns, and develop models for portfolio construction and return attribution for strategies
• Manage relationships with Portfolio Managers and work with them to grow and improve profitability of their business.
• Take responsibility for equity risk, interface with PMs and ensure that views / themes are accurately represented within portfolios
• Instruct and provide guidance related to the use of tools and concepts
• Expertly leverage resources, analytics and insights to improve portfolio construction, hedging techniques and strategies
• Collaborate with other Quantitative Researchers across Risk & Development, Data, Product and Engineering teams on accessing data sets and developing tools that improve Portfolio efficiency, profitability, etc.

Requirements

• PhD (ideal) or Master's degree in Mathematics/Statistics/Sciences and practical experience applying Quantitative Finance theories.
• Minimum 10 years' experience with at least 5 years working on a team focusing on Risk/Portfolio construction and analytics related to Equity Long/Short or other alternative Equity strategies.
• Strong Python skills – including prior experience with time series analysis, managing data, and contributing to internal analysis libraries in Python.
• Experience with SQL (data access and management) and proficiency with software development process and tools such as GitHub.
• Solid experience implementing statistical models with an in-depth understanding of factor models and their applications