Quantitative Researcher – Paris

The Role

As a Quantitative Researcher, you will focus on identifying and extracting predictive signals from a broad universe of datasets, including traditional financial inputs and high-dimensional alternative data.

You will design, test, and refine systematic strategies that directly contribute to live trading performance. Working within a large, high‑calibre research group, you will collaborate closely with data and software engineers to ensure seamless deployment of robust, scalable trading signals.

Key Responsibilities

Push the research frontier

Develop and apply advanced statistical, econometric, and machine learning methods to uncover persistent inefficiencies in complex, noisy datasets. Continuously iterate models with a strong focus on robustness, stability, and real-world performance.

Generate and validate alpha ideas

Originate differentiated investment hypotheses. Build rigorous research frameworks and conduct deep empirical testing, including large‑scale backtesting and out‑of‑sample validation.

Work with large structured and unstructured datasets to identify non‑obvious predictive structure. Engineer features and systematically evaluate signal quality across regimes.

Deploy research into production

Translate validated research into live trading signals. Partner with engineering teams to implement, monitor, and enhance strategies in production environments.

Candidate Profile

You are likely already operating in a highly quantitative environment and are looking for a step‑change in research scope and impact.

• PhD in Machine Learning, Artificial Intelligence, Mathematics, Physics, Statistics, Economics, Computer Science, Engineering, or a closely related quantitative discipline
• Postdoctoral or equivalent research experience in academia or industry
• Experience at a leading systematic hedge fund, prop trading firm, or quantitative research group is strongly preferred (e.g. multi‑strategy platforms, systematic macro pods, high‑frequency or mid‑frequency trading firms)
• Strong understanding of machine learning and/or econometric methods, with the ability to adapt and extend techniques for real‑world financial data
• Proven experience working with large‑scale, complex datasetsAdvanced programming ability in Python
• High level of rigor, independence, and intellectual curiosity, with the ability to operate in fast‑moving research environments
• Strong communication skills and ability to work effectively across research and engineering teams

A genuine interest in financial markets is expected. Prior experience in finance is beneficial but not mandatory for exceptional academic candidates.