Quantitative Credit Analyst

Laz Partners; Zürich; On-site; CHF 120,000 - 190,000; Full time

Job summary

Laz Partners in Zurich seeks a Quantitative Credit Analyst to join its Fixed Income team. You will develop quantitative models and Python-based tools to inform investment decisions, valuations and risk analysis across public and private credit markets.

You will collaborate with Portfolio Managers, applying advanced mathematics and statistics to bond pricing, yield curves and credit spreads, while building scalable analytics for diverse debt instruments.

Qualifications

• Advanced degree or strong academic credentials in a highly quantitative discipline.
• Experience applying quantitative methods to fixed income or credit investments.
• Strong programming skills in Python with practical use of NumPy/Pandas.

Responsibilities

• Develop, enhance and maintain quantitative models for public and private credit assets.
• Support portfolio analytics, pricing, risk assessment and scenario analysis.
• Design Python-based tools and automated processes to improve efficiency and accuracy.

Skills

Python programming

Quantitative modelling

Fixed income analytics

Credit portfolio analytics

Statistical modelling

Education

Mathematics

Physics

Engineering/CS/Financial Engineering

Quant finance or related field

Tools

NumPy

Pandas

Job description

Role Overview

We have partnered with a leading global asset manager looking to hire a Quantitative Credit Analyst to join its growing Fixed Income team in Zurich. The team manages portfolios worth several billion USD across public and private credit markets, with assets under management continuing to grow significantly.

The successful candidate will work closely with experienced Fixed Income Portfolio Managers across a broad range of credit asset classes. The role will focus on developing quantitative models, analytics and Python-based tools to support investment decisions, portfolio construction, valuations and risk analysis across both public and private credit markets.

The ideal candidate will have a strong quantitative and academic background, excellent Python skills, and experience applying quantitative techniques to fixed income or credit investments. Candidates with experience in public credit, private credit, or a combination of both will be considered, provided they demonstrate strong mathematical foundations and an understanding of fixed income markets.

Key Responsibilities

• Quantitative Modelling & Analytics: Develop, enhance and maintain quantitative models and analytical tools to support investment decisions across a broad range of public and private credit assets.
• Public Credit Analytics: Work closely with Fixed Income Portfolio Managers covering investment grade corporate bonds, government bonds, high yield credit, leveraged loans and other liquid fixed income instruments, supporting portfolio analytics, pricing, relative value and risk assessment.
• Private Credit Analytics: Support investment teams managing private debt portfolios spanning direct lending, infrastructure debt, real estate debt, private placements, asset-backed finance, fund finance and other structured credit investments.
• Fixed Income Modelling: Build and enhance analytical frameworks covering bond pricing, yield curves, duration, convexity, credit spreads, cash flow modelling and interest rate sensitivities.
• Python Development & Automation: Design and implement Python-based quantitative tools, models and automated analytical processes to improve the efficiency, accuracy and scalability of portfolio analysis.
• Portfolio Analytics & Risk: Develop tools to assess portfolio exposures, credit sensitivities, relative value, scenario analysis and risk characteristics across different fixed income asset classes.
• Cross-Team Collaboration: Partner closely with Portfolio Managers and investment professionals across public and private credit to deliver actionable quantitative insights and analytical solutions.

Requirements & Qualifications

• Experience: Ideally 2 to 7 years of relevant experience in quantitative research, fixed income analytics, credit portfolio analytics, quantitative development, or a related role within an asset manager, investment bank or other financial institution.
• Academic Background: Strong academic credentials, ideally with a degree in Mathematics, Physics, Engineering, Computer Science, Financial Engineering, Quantitative Finance or another highly quantitative discipline.
• Credit Markets Knowledge: Experience working with public and/or private credit investments, with a good understanding of fixed income instruments, their underlying characteristics, and the key drivers of risk and return.
• Fixed Income Mathematics: Strong understanding of bond mathematics, including pricing, yield curves, duration, convexity, spread duration, DV01, interest rate sensitivities and credit spread analysis.
• Python Programming: Excellent Python programming skills are essential, with demonstrated experience developing quantitative models, analytical tools or automated processes. Experience with libraries such as NumPy and pandas is highly advantageous.
• Quantitative Expertise: Strong mathematical and analytical capabilities, with knowledge of statistical modelling, numerical methods, probability, optimisation, Monte Carlo simulations or other quantitative techniques.
• Data Analysis: Ability to work with large and complex financial datasets, develop sound analytical frameworks, and translate quantitative outputs into practical investment insights.
• Problem-Solving: Strong intellectual curiosity and the ability to approach complex, sometimes loosely defined analytical problems independently, developing practical and efficient solutions.
• Communication & Collaboration: Ability to work effectively with Portfolio Managers and investment professionals, clearly communicating quantitative concepts and analytical findings.
• Cultural Fit: Highly motivated, intellectually curious, detail-oriented, and comfortable working in a dynamic, collaborative and high-performing investment environment.

The successful candidate will join a market-leading Fixed Income team at a global asset manager, gaining exposure to a diverse range of public and private credit investments while helping to develop and expand the team's quantitative analytics and modelling capabilities.