Role Description
**What you will gain from this experience**
We have an exciting
**2-month summer Internship**
opportunity that will provide hands-on experience within our Global Asset Allocation (GAA) Team in Madrid.
Interns will contribute directly to the Systematic/Quantitative pillar of the team, supporting the development of systematic investment models and tools used in multi-asset portfolio construction and monitoring.
You will gain exposure to real investment processes, learn how quantitative research is translated into robust, production-grade tools, and collaborate closely with experienced quants on a broad range of initiatives—from research and backtesting to automation, reporting, and scalable implementation within our Strategic and Tactical Asset Allocation (SAA and TAA) activities.
**Internship location**
Based in Madrid, you will become part of an international business working with teams across different geographies.
**What You Will Do**
* Support quantitative research and prototyping of systematic models used in multi-asset investing, portfolio construction and monitoring (e.g., forecasting, signals, portfolio construction, back-testing, performance attribution).
* Build and evaluate machine learning methods that enhance investment research and production workflows, with a strong focus on robustness, interpretability, and sound validation practices.
* Prototype AI tools that streamline analysis and reporting, turning research outputs into scalable, production-ready components.
* Help maintain and improve research codebases and analytics pipelines (data ingestion, cleaning, validation, and reproducible experiments).
**What We Are Looking For**
* Ready to start the two-month summer program in June 2026\.
* Undergraduate finishing your studies in 2027 (Going into your final year at university or a master’s degree after the internship).
* Strong analytical skills.
* Confident using the Microsoft Office suite, in particular Excel.
* Clear written and verbal communication.
* Ability to plan your time and manage competing priorities within a fast-paced environment.
* Strong Python skills for data analysis and modelling.
* Solid foundations in machine learning, statistics, probability and optimization; familiarity with time series, stochastic modelling and Artificial Intelligence (LLMs) is a plus.
* Experience with version control (Git) and good software engineering practices (clean code, testing, documentation).
* Interest in applying AI/ML techniques to real-world financial problems.
* Comfort working with data sources and databases, such as SQL and/or NoSQL (e.g., MongoDB).
**Why Join Us**
* Work on real, production-relevant quantitative projects within a multi-asset investment team, with direct exposure to how systematic research impacts portfolio decisions.
* Learn from experienced practitioners and gain a broad view of advanced quant initiatives.
* Build practical skills at the intersection of AI and investment management, from research to implementation, in a collaborative and international environment.