Fixed Income Research Analyst
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Key skills for this role
About the Role
Revolut is looking for a Senior Fixed Income Research Analyst (Quant Researcher) to develop systematic predictive models for corporate bonds and credit default swaps. You will design optimal execution and basket-selection strategies, integrating macroeconomic trends.
Key Skills for This Role
Responsibilities
- Leading the development of systematic predictive models and strategies for corporate bonds and credit default swaps
- Integrating macroeconomic trends and sector specific insights into the portfolio engine
- Designing and implementing fixed income portfolio construction and optimisation frameworks
- Collaborating with engineers to design, test, and refine algorithms for automated execution workflows
- Conducting hypothesis testing and backtesting to validate model accuracy, performance, and transaction costs
- Managing practical execution specifics, including liquidity sourcing, market impact, and transaction costs
- Ensuring full compliance with regulatory standards, portfolio risk limits, and financial promotions
Requirements
- 5+ years of experience in quantitative investment research and systematic portfolio construction for fixed income
- In depth understanding of corporate credit market microstructure and OTC bond liquidity constraints
- Solid knowledge of quantitative and statistical tooling applied to systematic investment decisions
- Advanced programming skills in Python and its scientific data stack
- Proven track record of handling transaction cost analysis, tracking error modelling, and risk attribution
- Educational background in STEM
Full Job Posting
About Revolut
- People deserve more from their money. More visibility, more control, and more freedom.
- Since 2015, Revolut has been on a mission to deliver just that.
- Our powerhouse of products — including spending, saving, investing, exchanging, travelling, and more — help our 75+ million customers get more from their money every day.
About The Role
- Our Wealth & Trading team is at the forefront of redefining how people invest, using data driven tools to make smarter financial decisions accessible to everyone.
- We’re looking for a Senior Fixed Income Research Analyst (Quant Researcher) to bridge the gap between alpha generation and execution engineering.
- You’ll be leading the development of systematic predictive models for corporate bonds and credit default swaps, and designing optimal execution and basket selection strategies.
What You'll Be Doing
- Leading the development of systematic predictive models and strategies for corporate bonds and credit default swaps
- Integrating macroeconomic trends and sector specific insights into the portfolio engine
- Designing and implementing fixed income portfolio construction and optimisation frameworks
- Collaborating with engineers to design, test, and refine algorithms for automated execution workflows
- Conducting hypothesis testing and backtesting to validate model accuracy, performance, and transaction costs
- Managing practical execution specifics, including liquidity sourcing, market impact, and transaction costs
- Ensuring full compliance with regulatory standards, portfolio risk limits, and financial promotions
What You'll Need
- 5+ years of experience in quantitative investment research and systematic portfolio construction for fixed income
- An in depth understanding of corporate credit market microstructure and OTC bond liquidity constraints
- Solid knowledge of quantitative and statistical tooling applied to systematic investment decisions
- Advanced programming skills in Python and its scientific data stack
- A proven track record of handling transaction cost analysis, tracking error modelling, and risk attribution
- An educational background in STEM
Nice to have
- Experience designing long short strategies or execution specifics for systematic bond portfolios
- Familiarity with the ETF ecosystem, index replication methodologies, and basket optimisation
- A CFA qualification or significant progress toward the certification
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