STAGE - Quantitative Researcher H/F

Crédit Agricole Group; Paris; Hybride; EUR 60 000 - 80 000; Temps partiel

Résumé du poste

A leading financial services group in Paris is seeking an intern for its Quantitative Research team to work on implementing a Decision-Focused Learning framework for portfolio optimization. The position offers professional growth opportunities, a telecommuting agreement, and an allowance to promote sustainable mobility. The ideal candidate will have an interest in quantitative research and investment strategies, supported by a mentor for skill development.

Prestations

Professional opportunities within the group

Telecommuting agreement

€600 to €700 allowance for sustainable mobility

Qualifications

• Interest in quantitative research and investment strategies.
• Familiarity with neural networks and portfolio optimization is a plus.

Responsabilités

• Implement and evaluate a Decision-Focused Learning framework for portfolio optimization.
• Bridge the gap between discrete choices and continuous optimization methods.

Description du poste

At Crédit Agricole Assurances, the Group Investment Department offers investment strategies to our various subsidiaries. It ensures their implementation and monitoring with a focus on acting in the best interest of society and our clients.

This internship is a joint project between the Quantitative Research team of Crédit Agricole Assurances and the CERMICS laboratory (École des Ponts ParisTech). The objective is to implement and evaluate a decision‑focused learning (DFL) framework applied to portfolio optimization.

The traditional way of building an investment portfolio is a two-step process that separates parameter estimation from optimization:

• Expected returns and dependence relationships (covariances) between assets are estimated
• These estimates are plugged into a mathematical model, to find the optimal mix of assets accordingly to the objective

The main issue with this “estimate-then-optimize” approach is that both steps are treated as separate tasks, leading to several issues, in particular with multivariate time series data. The internship objective is to implement and evaluate a Decision‑Focused Learning (DFL) framework aiming to tackle the flaws of the traditional “estimate, then optimize” method. Instead, DFL core idea is to create a single, end‑to‑end system that learns to make the best possible investment decisions directly from data.

Portfolio optimization often involves making discrete (combinatorial) choices. However, the mathematical methods typically used to train neural networks work best with smooth, continuous problems, not discrete, combinatorial ones. A key challenge will be to bridge such gap and identify the most suitable algorithm from the existing literature for training a network that includes a combinatorial optimization layer, specifically tailored to the unique uncertainties inherent in portfolio optimization.

Your internship mentor will support your integration and skill development.

BENEFITS

Professional opportunities within our Insurance entities and the Crédit Agricole group, a telecommuting agreement, a €600 to €700 allowance to promote sustainable mobility, and much more to discover!

This year, over 100 interns have been welcomed at the heart of our teams. A concrete way to support young people entering the job market, in line with the commitments outlined in the Crédit Agricole Group Youth Plan.

Principles of environmental protection and social responsibility are integrated at the core of our activities.

All of our offers are open to people with disabilities.