Quantitative Analyst

Job Overview:

To support investment decision-making by developing, testing, and maintaining quantitative models and strategies using statistical and mathematical techniques. The Quantitative Analyst will collaborate with portfolio managers, traders, and researchers to identify alpha-generating opportunities and improve risk-adjusted returns.

Job Responsibilities:

• Model Development: Design, implement, and maintain quantitative models for asset allocation, alpha signals, portfolio optimization, and risk management.

• AI Integration: Leverage machine learning and AI tools to extract insights from large datasets and improve forecasting accuracy.

• Data Analysis: Analyze financial, macroeconomic, and alternative data using Python/R, Bloomberg/BQuant tools.

• Back-testing: Build robust back-testing frameworks for quantitative investment strategies; monitor performance and drawdowns.

• Research & Insights: Apply advanced statistical techniques, machine learning, and econometric methods to investment research.

• Automation & Tools: Develop and maintain internal tools and dashboards to support real-time analytics, model updates, and reporting.

• Collaboration: Work closely with portfolio managers and the investment team to translate quantitative models into actionable investment ideas and create AI-powered investment products.

Qualification:

• Bachelor’s Degree from an accredited institution in Quantitative Finance, Computer Science, Mathematics, Statistics, Engineering, or related field.
• Master Degree, CQF and CFA will be a plus.

Experience:

• Minimum 3 years of experience in a quantitative role, preferably within asset management, funds, or investment banks.

Key Skills:

• Strong programming skills in Python, R, MATLAB, or C++; experience with data visualization and dashboarding tools is an advantage.
• Solid understanding of financial instruments, market microstructure, and investment theory.
• Expert use of Bloomberg Terminal, BQuant, and handling of financial APIs and time series data.
• Hands-on experience applying machine learning techniques to financial problems.
• Ability to clearly present complex quantitative findings to non-technical stakeholders.

Due to the high demand of applications, only those who meet the requirements for this role will be contacted.