Quantitative Risk Analyst / Research Analyste comportamental

• Strong quantitative and analytical skills
• Proficiency in programming languages
• Solid understanding of financial markets

Role Description

The Quantitative Risk Analyst / Research Analyst is responsible for developing and applying quantitative methodologies to assess, monitor, and manage financial risks while supporting investment research and decision-making. This role combines rigorous data analysis with financial theory to generate insights into market behavior, portfolio performance, and risk exposure.

Key responsibilities include building and validating risk models, conducting statistical analysis, and performing stress testing and scenario analysis to evaluate potential market impacts. The analyst works with large and complex datasets to identify patterns, correlations, and emerging risks, translating findings into clear and actionable recommendations.

This role also supports research initiatives by analyzing financial instruments, market trends, and macroeconomic factors. Collaboration with portfolio managers, risk teams, and other stakeholders is essential to ensure that quantitative insights are effectively integrated into investment strategies and risk frameworks.

In addition, the position involves monitoring model performance, enhancing analytical tools, and contributing to the continuous improvement of risk management processes. Strong attention to detail, analytical discipline, and the ability to interpret results within a broader financial context are critical for success.

Qualifications

• Bachelor’s or Master’s degree in Quantitative Finance, Mathematics, Statistics, Economics, Financial Engineering, or a related field.
• Strong quantitative and analytical skills, with expertise in statistical modeling and data analysis.
• Proficiency in programming languages such as Python, R, MATLAB, or similar tools.
• Solid understanding of financial markets, derivatives, and risk management concepts.
• Familiarity with risk metrics such as VaR, CVaR, stress testing, and scenario analysis.
• Ability to work with large datasets and apply advanced analytical techniques, including machine learning where applicable.
• Strong problem-solving skills and attention to detail.
• Effective communication skills to present complex quantitative findings in a clear and concise manner.
• Ability to collaborate across teams and contribute to research-driven decision-making.
• Commitment to continuous learning, innovation, and analytical excellence.

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