A globally established sovereign investment fund headquartered in Singapore, managing assets across more than 40 countries. The client’s Fixed Income & Multi Asset division invests across interest rates, credit, currencies, and other sectors. The team is known for blending deep fundamental insights with advanced quantitative strategies in managing corporate credit portfolios.

This role provides the opportunity to apply cutting-edge quantitative and machine learning techniques within a high-impact investment setting. The candidate will collaborate closely with top-tier portfolio managers and gain exposure to complex credit instruments, while developing tools that directly shape portfolio performance and risk outcomes. The firm also offers a dynamic and flexible work environment committed to professional growth.


Key Responsibilities:
- Design and implement quantitative models and tools to support alpha generation and enhance risk management in corporate credit portfolios.
- Develop and deploy machine learning models for credit default prediction and credit risk assessment.
- Apply advanced statistical and mathematical techniques to optimize portfolio construction and decision-making.
- Leverage cloud computing and big data tools to manage and analyze large fixed income datasets efficiently.
- Partner with technology and trading teams to integrate quantitative solutions into business operations, and clearly communicate insights to both technical and non-technical stakeholders.

Key Requirements:
- Bachelor’s or Master’s in a quantitative discipline (e.g., Mathematics, Statistics, Engineering, Computer Science).
- Strong proficiency in Python and experience deploying financial models into production.
- Familiarity with fixed income instruments (bonds, loans, CDS, convertibles) and core credit risk principles.
- Solid foundation in credit analysis including leverage metrics, cash flow forecasting, and credit ratings.
- Excellent communication and collaboration skills. CFA certification and experience with tools like Bloomberg/BQuant, BRS, or optimisation libraries are advantageous.


If you are interested, please reach out to Asher Tan at asher.tan@annexion-partners.com