Quantitative Risk Analyst (M/F)
Founded in 2000, Lunalogic is a consulting firm structured around two areas of expertise: Finance, Risk & Regulatory - supporting financial institutions, insurers, and industrial companies on risk, compliance, and quantitative modeling - and Data Science, Artificial Intelligence & Blockchain - designing and deploying advanced solutions in machine learning, deep learning, data engineering, and blockchain for all sectors.
For over 20 years, we have been developing sophisticated algorithms and infrastructures that allow for the exploitation of the potential of data science and blockchain on concrete issues: quantitative modeling, business process automation, fraud detection, optimization of industrial systems, or predictive analysis. We work with major accounts and SMEs in finance, banking, insurance, healthcare, and industry, with references such as Mérieux, Limagrain, AXA, Servier. Our strength: R&D and innovation, and continuous training to attract the best talents from the most prestigious educational backgrounds.
At Lunalogic, we transform our clients' technological and financial challenges into concrete, operational, and high-impact solutions, within a stimulating and collaborative environment.
Position: Quantitative Risk Analyst (M/F) - Mission at a Major Account client
As part of its development, Lunalogic is looking for a quantitative analyst to work with a client in the finance / commodities / risk management sector, within a Risk or R&D team in charge of measuring and managing market exposures.
Missions The consultant will work on risk measurement and management issues:
Development and improvement of risk measurement tools (VaR, stress-testing, scenarios) Construction of Python infrastructures for the automation of risk calculations and reporting Sensitivity analysis of portfolios to market factors (price, volatility, rates, FX) Contribution to risk allocation and performance decomposition models Close collaboration with Front Office, Risk, and IT teams Participation in the continuous improvement of existing tools and methodologies
Profile sought
Master's degree (engineering school or university) with a specialization in financial engineering, quantitative finance, or applied mathematics First significant experience (internship or apprenticeship) in risk management, quantitative research, or systematic trading Experience in an international financial environment is appreciated
Required skills
Solid mastery of Python (automation, risk calculation, data pipelines) Good knowledge of numerical and statistical methods (Monte Carlo, historical simulations, optimization) Knowledge of stochastic calculus and derivatives pricing Practice of SQL and market tools (Bloomberg) Fluent English mandatory; French appreciated Excellent written and oral communication, rigor, and analytical mindset