- Collaborate with Portfolio Managers and Analysts to design and implement models and tools that enhance risk management strategies and drive alpha generation
- Leverage quantitative methodologies and discretionary insights to monitor and optimize credit portfolios, focusing on performance enhancement through robust portfolio construction.
- Design and refine machine learning models for corporate credit, emphasizing default probability modelling and credit risk assessment to improve decision-making.
- Apply advanced mathematical and statistical techniques to solve complex problems and make data-driven decisions.
- Utilize cloud-based solutions and data analytics tools to efficiently manage and analyse large datasets.
- Communicate complex quantitative concepts effectively to stakeholders, including traders and risk managers, while collaborating with technology and trading teams to integrate models into broader business processes.
What makes you a successful candidate
- Bachelor's or Master's degree in a quantitative field (Mathematics, Statistics, Physics, Engineering, Computer Science, or related).
- Proficient in Python programming with experience in developing and deploying financial models to production.
- Familiarity with financial instruments, including bonds, loans, CDS (Credit Default Swaps), and convertible securities, along with a strong understanding of risk management principles.
- Strong grasp of credit analysis fundamentals, including leverage ratios, cash flow assessment, and market-implied metrics (e.g., CDS spreads, credit ratings) for evaluating creditworthiness and investment risk.
- Strong mathematical and statistical skills.
- Excellent communication and teamwork skills, capable of conveying complex concepts to non-technical stakeholders.
- CFA certification, experience with underwriting bonds or related credit analysis, bloomber/BQuant, BlackRock Solutions (BRS), and familarity with optimisation software/packages would be advantageous
