Stage - Quantitative Researcher H/F - Crédit Agricole Assurances

Grâce à notre modèle de bancassurance, à notre expertise et à l'engagement de nos 6700 collaborateurs, en France et dans autres 9 pays, nous sommes le 1er assureur en France et le 1er bancassureur européen. Crédit Agricole Assurances accompagne ses clients - particuliers, professionnels, agriculteurs et entreprises - à chaque instant de leur vie, à travers trois métiers (Epargne/Retraite, Prévoyance/Emprunteur/Collectives, Dommages). Nous leur proposons une gamme complète de produits et services distribués dans les Caisses régionales de Crédit Agricole et chez LCL.

En agissant chaque jour dans l'intérêt de la société, nous sommes un groupe engagé en faveur des diversités et de l'inclusion et plaçons l'humain au coeur de toutes nos transformations. Tous nos postes sont ouverts aux personnes en situation de handicap.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:
1. Expected returns and dependence relationships (covariances) between assets are estimated
2. 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 intership 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 include 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.

Critères de candidature

:

Cadre / Non Cadre : Non cadre

Niveau d'étude minimum : Bac +5 / M2 et plus

Formation / Spécialisation :
Grande Ecole d'Ingénieur, Master en recherche opérationnelle (RO), mathématiques de modélisation ou vision apprentissage
A first professional experience is not required for the role. Curiosity, creativity and proactivity are essential

Niveau d'expérience minimum : 0 - 2 ans

Soft skills :
Technical skills :
Statistics and probabilities, Machine learning, deep learning, data science

Behavioral skills :
- Communicate with ease in writing
- Communicate with ease orally
- Organize and carry out work with rigor, autonomy, and the necessary discretion
- Propose initiatives or solutions
- Work in a project-based mode

Ability to write clear code

Outils informatiques :
Python