Quantitative Risk Analyst

Paragon Alpha - Hedge Fund Talent Business

Greater London

Vor Ort

GBP 70.000 - 110.000

Vollzeit

Vor 2 Tagen
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Zusammenfassung

Paragon Alpha - Hedge Fund Talent Business is seeking a highly quantitative Risk Analyst to join its Risk team. The role blends quantitative risk analysis with hands-on development of risk systems and analytics used to measure and manage portfolio risk across multiple strategies.

The candidate will contribute to the design and enhancement of the firm's risk infrastructure, working across credit, equities and derivatives in a sophisticated buy-side environment.

Qualifikationen

  • Advanced degree in quantitative fields (MS/PhD).
  • 3–7 years in quantitative risk, risk strats or quantitative development.

Aufgaben

  • Apply statistical and quantitative techniques to monitor market, credit and liquidity risk across a multi‑strategy portfolio.
  • Develop and enhance quantitative risk models and risk analytics across credit, equities and derivatives.
  • Design scenario analyses, stress tests and risk attribution frameworks for CRO and PMs.
  • Collaborate with Portfolio Management, Operations and Technology to implement risk analytics.
  • Build data pipelines and SQL/database infrastructure for daily risk reporting and limits monitoring.
  • Automate risk workflows to improve accuracy, scalability and efficiency.

Kenntnisse

Python
C++/C#
SQL
Time-series

Ausbildung

MS/PhD in Quantitative Finance/related

Jobbeschreibung

An established, credit-focused multi-strategy investment manager is seeking a highly quantitative Risk Analyst to join its Risk team.

The role combines quantitative risk analysis with hands-on development of risk systems, models and analytics used to measure and manage portfolio risk. The successful candidate will contribute to the design, implementation and ongoing enhancement of the firm's risk infrastructure, working across credit, equities and derivatives.

This is a technically demanding position requiring strong programming capabilities, quantitative expertise and a genuine understanding of investment risk. It offers the opportunity to work closely with Risk and Portfolio Management teams in a sophisticated buy-side environment.

Key Responsibilities
  • Apply statistical and quantitative techniques to monitor market, credit and liquidity risk across a multi-strategy portfolio.
  • Contribute to the development and enhancement of quantitative credit and convertible securities models used to support investment screening and security selection.
  • Design scenario analyses, stress tests and risk attribution frameworks to support the CRO, Portfolio Managers and senior investment professionals.
  • Collaborate with Portfolio Management, Operations and Technology teams to ensure effective implementation of risk analytics and systems.
  • Design, develop and maintain quantitative risk models, systems and analytics across credit, equities and derivatives.
  • Build and support production risk calculations covering exposures, sensitivities, stress testing, scenario analysis, liquidity and P&L attribution.
  • Develop robust data pipelines and SQL/database infrastructure to support daily risk reporting and limit monitoring.
  • Automate and enhance risk workflows to improve accuracy, scalability and efficiency.
Qualifications
  • Advanced degree (MS or PhD) in Quantitative Finance, Financial Engineering, Computer Science, Mathematics, Statistics, Physics or a related discipline.
  • Approximately 3–7 years of relevant experience in quantitative risk, risk strats or quantitative development within a hedge fund, asset manager or investment bank. Candidates outside this range will also be considered.
  • Strong programming proficiency in Python, C# or C++, alongside SQL and database technologies.
  • Solid understanding of quantitative methods, including pricing and valuation, statistics, time-series analysis and simulation.
  • Demonstrable experience designing, developing or maintaining production risk systems, quantitative models or risk analytics.
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