Quantitative Engineer for CDS Curve Modelling
Background
We are looking for a candidate with a background in finance at banks, hedge funds, asset managers who specialises in fixed income or rates with evidence of having built or maintained pricing, risk or curve infrastructure in production. Academic background or professional training in quantitative finance, financial engineering, mathematics, physics or similar who thinks like a modeller and an engineer, not just a coder and is able to articulate complex financial concepts clearly to non-specialists without dumbing them down.
Core Domain Expertise
- Deep, demonstrable experience in credit derivatives.
- Strong understanding of bond and CDS pricing, Z spreads, ASW spreads, CSD basis, hazard rate term structures, survival curves, discounting and recovery assumptions.
- Direct familiarity with PECS or par equivalent CDS methodologies, or equivalent CDS to bond curve alignment techniques.
- Theoretical grounding in credit risk modelling, term structure construction, interpolation and extrapolation methods.
- Ability to reason about market conventions, liquidity constraints and model tradeoffs rather than treating curve building as a mechanical exercise.
Quantitative and Engineering Skills
- Advanced Python for numerical modelling, calibration routines and analytics libraries.
- Strong C++ for performance critical components or reusable pricing libraries.
- Experience implementing curve bootstrapping, calibration loops, optimization and numerical solvers.
- Comfort working with or extending existing quant libraries such as QuantLib or proprietary frameworks.
- Clean, testable, well structured code with clear separation between model logic and presentation layers.
Technology Stack
- Python: NumPy, SciPy, pandas, matplotlib or equivalent numerical and visualization tools.
- C++17 or later, with attention to performance and memory management.
- Familiarity with REST APIs or internal service architectures for model consumption.
- Version control using Git with disciplined commit and review practices.
UX/UI and Visualization Capability
- Proven ability to translate complex quantitative outputs into intuitive, low cognitive load interfaces.
- Experience designing dashboards, visualizations or analytic tools for non-quant users.
- Strong instinct for simplifying multidimensional curve data into clear charts, tables and explanatory views.
- Ability to collaborate with product or frontend teams to ensure models are usable, not just correct.
Apply Now
Apply Now
We are looking for a candidate with a background in finance at banks, hedge funds, asset managers who specialises in fixed income or rates with evidence of having built or maintained pricing, risk or curve infrastructure in production. Academic background or professional training in quantitative finance, financial engineering, mathematics, physics or similar who thinks like a modeller and an engineer, not just a coder and is able to articulate complex financial concepts clearly to non-specialists without dumbing them down.
Core Domain Expertise
- Deep, demonstrable experience in credit derivatives.
- Strong understanding of bond and CDS pricing, Z spreads, ASW spreads, CSD basis, hazard rate term structures, survival curves, discounting and recovery assumptions.
- Direct familiarity with PECS or par equivalent CDS methodologies, or equivalent CDS to bond curve alignment techniques.
- Theoretical grounding in credit risk modelling, term structure construction, interpolation and extrapolation methods.
- Ability to reason about market conventions, liquidity constraints and model tradeoffs rather than treating curve building as a mechanical exercise.
Quantitative and Engineering Skills
- Advanced Python for numerical modelling, calibration routines and analytics libraries.
- Strong C++ for performance critical components or reusable pricing libraries.
- Experience implementing curve bootstrapping, calibration loops, optimization and numerical solvers.
- Comfort working with or extending existing quant libraries such as QuantLib or proprietary frameworks.
- Clean, testable, well structured code with clear separation between model logic and presentation layers.
Technology Stack
- Python: NumPy, SciPy, pandas, matplotlib or equivalent numerical and visualization tools.
- C++17 or later, with attention to performance and memory management.
- Familiarity with REST APIs or internal service architectures for model consumption.
- Version control using Git with disciplined commit and review practices.
UX/UI and Visualization Capability
- Proven ability to translate complex quantitative outputs into intuitive, low cognitive load interfaces.
- Experience designing dashboards, visualizations or analytic tools for non-quant users.
- Strong instinct for simplifying multidimensional curve data into clear charts, tables and explanatory views.
- Ability to collaborate with product or frontend teams to ensure models are usable, not just correct.
Apply Now
Apply Now