Credit Quantitative Analyst (CBBC369)

Referment; Greater London; On-site; GBP 90,000 - 130,000; Full time

Job summary

Referment is a London-based manager focusing on fixed income, macro and systematic strategies. The role sits at the intersection of data engineering and quantitative structuring, owning the integrity of core credit datasets and extending pricing capabilities into new products.

You will operate a live data pipeline, reconcile multi-source data, and build pricers for ETFs and swaps, with scope to add CDS and cash bond coverage. A quantitative degree and strong Python skills are required.

Qualifications

• Proven experience building point-in-time credit datasets through events and index rolls.
• Experience producing ETF total-return series with rates separated from credit return.
• Strong understanding of CDS option and tranche pricing models.

Responsibilities

• Build and maintain historical credit datasets across products (indexes, defaults, recoveries, corporate actions).
• Reconcile data across sources and dealer marks to stay consistent and accurate.
• Operate live credit data pipeline, ensuring input quality and derived risk analytics.
• Build pricers and spread construction for limited coverage instruments (credit ETFs, total return swaps).
• Extend coverage to credit tranches, CDS curves, and mapped cash bonds; potential future CDS futures/ETFs.

Skills

Python

Data pipelines

Credit datasets

Reconciliation

Pricing models CDS

Education

Quantitative degree

Tools

CDS pricing

ETFs

Job description

Referment is working with a London-based alternative investment manager that runs fixed income, macro and systematic strategies for institutional investors. Its credit business is strengthening the foundational data and product infrastructure behind its pricing and risk analytics, and this role sits at the intersection of data engineering and quantitative structuring.

You will own the integrity of the core credit datasets while extending pricing capability into products the desk cannot yet cover cleanly. It is a hands-on mandate, best suited to someone who has built and operated credit data infrastructure in a live production environment rather than only consumed it for research.

The Role

• Build and maintain the historical dataset across all credit products, covering index rolls, defaults and recoveries, and corporate actions.
• Reconcile data across multiple sources and dealer marks so the dataset stays consistent and accurate.
• Operate the credit dataset as a live pipeline, owning day-to-day quality of both raw inputs and derived risk analytics.
• Build pricers and spread construction where coverage is limited, including credit ETFs and total return swaps, with the rates component cleanly separated from the credit return.
• Extend coverage to credit tranches, clean single-name CDS curves and cash bonds mapped to the CDS universe, with scope to add credit futures and total return swaps on ETFs.

What We’re Looking For

• Proven experience building and maintaining point-in-time credit datasets through actual credit events and index rolls.
• Experience producing ETF total-return series with the rates leg separated from the credit return.
• A strong grasp of CDS option and/or tranche pricing models, even if you would not build them from scratch.
• Comfort working across multiple data vendors and dealer sources, with strong reconciliation and data-quality instincts.
• Solid programming skills in Python or similar for pricing and data pipeline tools.
• A degree in a quantitative discipline.

This could suit a quantitative developer, credit quant or data engineer who has worked on credit or fixed income data platforms and wants broader ownership of both the data and pricing stack.

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