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Quantitative Analyst – Credit, Global Asset Manager

Job in Greater London, London, Greater London, W1B, England, UK
Listing for: Logansinclair
Full Time position
Listed on 2026-04-28
Job specializations:
  • Finance & Banking
    Data Scientist, Risk Manager/Analyst, Banking Analyst, FinTech
Salary/Wage Range or Industry Benchmark: 60000 - 80000 GBP Yearly GBP 60000.00 80000.00 YEAR
Job Description & How to Apply Below
Location: Greater London

Quantitative Analyst – Credit, Global Asset Manager

Location London

Compensation Competitive

Description

Our client is a global asset manager with a leading systematic credit capability. They seek a Quantitative Analyst to enhance alpha research, modelling and the research platform across investment grade, high yield and structured products. The role blends research, implementation and close partnership with portfolio teams.

Responsibilities
  • Develop quantitative credit models, including spread and default risk frameworks.
  • Source, test and integrate new alpha signals across credit markets into research pipelines.
  • Monitor model-generated trade ideas and disseminate outputs to investment teams.
  • Produce rigorous standalone research with clear reports and presentations.
  • Contribute thematic quantitative research and thought leadership across credit.
  • Build and maintain the research platform and shared analytical codebase.
  • Improve communication and visualisation of model outputs for portfolio teams.
  • Uphold code quality, version control and systematic testing standards.
  • Translate quantitative insights for Portfolio Managers, Credit Analysts, Traders and Investment Directors.
  • Engage in cross-asset forums and collaborate across regions and disciplines.
  • Support the internal and external profile of the quantitative research function.
  • Discuss model methodology and research outputs with clients when required.
Requirements
  • BSc/MSc in a quantitative discipline; strong statistics, econometrics and numerical methods.
  • Proven track record building quantitative models in an investment or credit context.
  • Strong Python (pandas, Num Py); SQL and Git advantageous.
  • Advanced Excel; familiarity with Bloomberg or similar data platforms.
  • Experience handling large, complex financial datasets.
  • Clear written and verbal communication of technical concepts.
  • Collaborative and adaptable, comfortable working across functions and asset classes.
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