Quantitative Analyst

Overview

As a Quantitative Analyst in the Investments Quantitative team, you will develop models and tools to support fixed income and pension liabilities, portfolio management, and risk analytics. You will work with cross-functional teams to deliver accurate, well-tested solutions and improve processes to reduce manual handovers. The role combines hands-on Python work with governance and collaboration across Investments, Risk, and Finance functions.

This is an opportunity to influence asset and liability modelling at scale within a leading retirement-focused financial group.

• annual bonus plan
• share schemes
• pension contribution
• life assurance
• healthcare plan
• 25 days holiday plus extra days

• Develop and enhance financial models for fixed income, liabilities, portfolio optimisation, risk analytics, and capital/SCR modelling
• Improve tools and processes, focusing on efficiency, new libraries, code quality, and testing
• Reduce manual handovers and duplication by supporting and automating tools
• Support Investments teams in day-to-day tool usage, troubleshoot, and implement methodological improvements
• Own platform issues and coordinate with in-house and vendor teams to resolve them
• Ensure changes comply with IT Change Management standards
• Collaborate with team to create robust, well-tested solutions for the Investments function
• Work closely with the business to improve tools used across the Investments function

• Python development experience with NumPy, SciPy, Pandas or Polars
• Strong programming fundamentals (algorithms, data structures, complexity)
• Knowledge of no-arbitrage, risk-neutral methods, vanilla derivative pricing, and VaR
• Experience with portfolio analytics, portfolio management, and relative value assessment
• Experience building Python applications or libraries and interfacing Excel with Python (COM/xlwings)
• Experience with SQL, Snowflake or similar platforms; cloud development or VBA is beneficial
• Experience with portfolio risk analytics tools such as BlackRock Aladdin, State Street Alpha, or SimCorp

• collaboration with cross-functional teams
• problem solving and analytical thinking
• attention to code quality and testing
• Python (NumPy, SciPy, Pandas or Polars)
• SQL / Snowflake
• Excel integration with Python (COM / xlwings)