The successful candidate will contribute to the development and maintenance of the team’s analytical infrastructure while supporting client-facing work with insurance partners across asset allocation, relative value, and scenario analysis.
Key responsibilities
- Develop, maintain, and enhance quantitative models in Python, including optimization engines, scenario generators, stress testing frameworks, and liability projection tools.
- Design and implement cross asset allocation strategies, with a particular emphasis on fixed income markets, including rates, credit, and structured products, and their treatment within an insurance balance sheet.
- Lead ALM and asset-liability matching analyses, translating outputs into portfolio recommendations suitable for client implementation.
- Contribute to risk-based capital modeling and ensure that capital considerations are properly integrated into allocation and relative value work.
- Conduct relative value, scenario, and stress testing analyses across fixed income and broader cross asset portfolios.
- Analyze publicly available insurer portfolio data to support peer bench-marking, prospecting, and thought leadership.
- Monitor developments in fixed income markets, insurance regulation, and the broader macroeconomic environment, and incorporate them into the team’s work.
- Present analytical findings to investment teams, clients, and senior stakeholders in a clear and structured manner.
Required skills and experience
- 3 to 6 years of relevant experience in a quantitative strategy, portfolio construction, or cross asset research role within asset management, insurance, or a sell-side equivalent.
- Strong proficiency in Python, with demonstrable experience building and maintaining production-quality quantitative models.
- Solid technical knowledge of fixed income, covering rates, credit, and spread products, together with the underlying mathematics.
- Working understanding of ALM principles and the interaction of asset and liability cash flows on an insurance balance sheet.
- Prior experience in quantitative strategy or cross asset research, gained within asset management, insurance, or an equivalent sell-side environment.
- Familiarity with regulatory capital frameworks such as Solvency II, ICS, or RBC is desirable.
- Strong statistical and mathematical foundations.
- Proven ability to communicate complex analysis clearly to both technical and non-technical audiences.
- Effective collaborator, comfortable engaging with stakeholders across different teams and levels of seniority.
