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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.
