Quantitative Risk Analyst

Join our dynamic team at IC as a Quantitative Risk Analyst and help shape the future of FinTech innovation. This full-time, on-site opportunity based in Limassol offers you the chance to make a real impact in a fast-paced and forward-thinking environment. Apply now and take the next step in your career with us! 

Who We Are: 

IC, a global leader in trading with over 15 years of success, a strong international presence, and a team of skilled professionals, remains at the forefront of financial technology innovation. As an agile company that values growth and collaboration, we offer an exciting opportunity to be part of a dynamic industry where innovation meets excellence.

What You’ll Do:

As a Quantitative Risk Analyst, you will be responsible for designing and validating sophisticated risk models—including VaR, Expected Shortfall, and Monte Carlo simulations—across diverse CFD asset classes like FX, equities, and crypto. You will develop real-time monitoring tools and Early Warning Indicators while performing rigorous stress tests and independent model validations to ensure robust risk governance. By translating complex quantitative insights into actionable data, you will directly support the firm’s risk appetite, margin policies, and capital-at-risk frameworks.

What We’re Looking For:

Model Development and Validation

• Design and implement quantitative risk models covering VaR, Expected Shortfall, Monte Carlo simulation and stress testing across CFD asset classes including FX, indices, commodities, equities and crypto.

• Develop and calibrate statistical models to support dynamic risk limit frameworks.

• Build anomaly detection, scenario generation and Early Warning Indicator (EWI) models to support real-time risk monitoring and automated alerting.

• Perform independent model validation across market risk, counterparty credit risk and trading algorithm models, including the development of benchmark models and performance metrics.

Risk Monitoring and Controls

• Monitor and periodically review margin rates across asset classes to ensure they remain appropriate relative to prevailing market conditions and volatility.

• Run stress testing and scenario analysis to assess the firm's overall risk exposure under adverse market conditions, including evaluation of risk appetite and risk limits.

• Identify and assess model risk across market, credit, liquidity, and operational risk areas, and put in place appropriate mitigating controls and escalation procedures.

Risk Governance and Capital Framework

• Ensure all risk models are properly documented, maintained in a formal risk register, regularly reviewed, and subject to structured oversight.

• Support the Risk Governance Framework, ensuring controls are in place across all risk types.

• Contribute to the identification and definition of risk parameters for each deployed strategy and execution venue, including capital at risk, exposure limits, drawdown limits, stress loss tolerances, and kill-switch triggers.

Stakeholder Communication

• Present model outputs, assumptions, and risk insights to stakeholders and senior management in a clear and practical way.

• Coordinate model deployment with the Quantitative Development team, managing the transition from research to production.
  

Qualifications:

Academic Background

• Degree in Quantitative Finance, Financial Mathematics, Statistics, Physics, Engineering, Computer Science or a similar field.

• Professional certification such as FRM (GARP) or CFA is advantageous.

Required

• Minimum 3 years of experience in quantitative model development, validation or quantitative risk management within financial services or a trading environment. Experience with CFD or FX derivatives is strongly preferred.

• Proven ability to develop, calibrate and validate quantitative risk models independently, from initial research through to production.

• Strong Python skills across pandas, NumPy, SciPy, scikit-learn, statsmodels and Jupyter, with practical experience handling large, high-frequency financial datasets.

• Experience with SQL is strongly preferred.

• Good foundation in probability theory, mathematical statistics, stochastic processes and time series econometrics as applied to financial modelling.

• Understanding of market microstructure concepts

• Working knowledge of standard market risk models including VaR, ES, Greeks, IV and Monte Carlo, as well as counterparty credit risk concepts such as PD, LGD, EL, and UL.

Working Schedule:

On-site - Monday to Friday; 09:00 – 17:00