Planning & Analysis Strats, Associate, Paris (Quantitative Engineering)

Location: Paris, Ile-de-France, France; office-based role

Company

hirify.global is a financial services firm with a centralized Core Engineering organization supporting risk, finance, compliance, treasury, human capital, and other core functions.

What you will do

• Research, design, build, implement, document, validate, deploy, and monitor quantitative models and macroeconomic and financial scenarios for budget planning, risk management, and regulatory stress testing.
• Develop statistical, econometric, time-series, and explainable machine-learning models to forecast revenues, expenses, balance-sheet items, and other financial variables, including structural breaks and regime changes.
• Manage data preparation, feature engineering, model selection, validation, testing, uncertainty quantification, deployment, and ongoing monitoring across the model-development lifecycle.
• Build scalable analytical tools, production-grade AWS Cloud infrastructure, and AI and agentic capabilities for scenario development, calibration, analysis, and reporting.
• Partner with business divisions, Finance, Risk, and other corporate functions to translate planning and risk-management requirements into quantitative methodologies and decision-ready analysis.
• Prepare clear, auditable materials for internal governance, Model Risk Management review, regulatory requirements, and remediation activities.

Requirements

• PhD in Statistics, Economics, Econometrics, Computer Science, Applied Mathematics, Physics, Financial Engineering, or a related quantitative field; no prior professional experience is required.
• Alternatively, a Master’s degree in a relevant quantitative field plus one year of experience, or a Bachelor’s degree plus three years of experience, in quantitative modeling, scenario design, financial engineering, quantitative development, or a related role.
• Programming experience with Rust, Python, or C++ for quantitative modeling, data analysis, scientific computing, model implementation, or agentic workflows.
• Experience developing, calibrating, testing, and documenting macroeconomic and financial scenarios and models, including forecasting financial variables with time-series, structural-break, and regime-switching techniques.
• Experience with feature engineering, variable and model selection, explainable machine learning, causal and non-parametric methods, hyperparameter tuning, validation, performance testing, Monte Carlo simulation, conformal prediction, monitoring, and technical documentation.
• Experience processing large structured and unstructured datasets using SQL and database technologies, and implementing scalable quantitative, machine-learning, and agentic solutions using AWS or similar cloud technologies.
• Ability to translate business, planning, scenario-design, and risk-management requirements into quantitative methods, technical specifications, and decision-ready senior-management reporting.

Nice to have

• Strong academic research background as a PhD graduate.
• For non-PhD candidates, publications, open-source contributions, or substantial quantitative projects demonstrating exceptional technical and research capability.

Culture & Benefits

• Work within The Core Engineering organization, comprising more than 2,000 engineers and strats.
• Collaborate across business divisions, Finance, Risk, and other corporate functions.
• Use cloud, AI, and machine learning to support scalable solutions and operational efficiency.