PhD/Post-Doctoral Researcher ML Quantitative Researcher – London/Paris

PhD/Post-Doctoral Researcher ML Quantitative Researcher – London/Paris Overview

We are seeking exceptional ML Quantitative Researchers to join a leading global market-maker in London or Paris. This permanent position offers the chance to engage in high-impact teams focused on systematic trading, predictive modeling, and machine learning research. The roles are situated in fast-paced, collaborative environments where research is directly linked to live profit and loss (PnL). Teams have diverse focuses, ranging from FX-driven research to multi-asset portfolio construction and optimization, and are looking for individuals with a strong academic background and a passion for financial markets.

Key Responsibilities

• Conduct statistical and machine learning research on large, high-dimensional datasets, including alternative data.
• Develop and improve predictive models, trading signals, and systematic strategies.
• Backtest and deploy models in live trading environments.
• Contribute to portfolio optimization and risk modeling.
• Collaborate with engineers and traders to refine models and drive performance.
• Continuously iterate based on model behavior, market dynamics, and new data.

Ideal Candidate Profile

• Currently completing or recently completed a PhD or Postdoc in mathematics, statistics, physics, computer science, engineering, or related quantitative fields.
• Strong background in statistical modeling, machine learning, and data analysis.
• Proficiency in Python and at least one compiled language (e.g., C++).
• Experience working in a data-driven research environment with practical application.
• Strong analytical thinking and a track record of solving complex problems.
• Excellent communication skills, able to clearly articulate complex ideas.

Preferred Experience

• Exposure to financial markets, portfolio construction, or trading strategy development.
• Familiarity with time-series analysis, natural language processing (NLP), or pattern recognition techniques.
• Experience with additional tools such as R, MATLAB, or machine learning frameworks.

Additional Achievements

• Participation or accolades in elite quantitative competitions (e.g., International Mathematical Olympiad, Putnam Competition, ICPC, Kaggle, or other national/international math and coding contests).
• Top academic performance, such as graduating first in class, Dean’s List, or ranked in the top percentile of degree cohort.
• Publication record in top-tier journals or conferences (e.g., NeurIPS, ICML, JMLR, etc.).
• Awards, fellowships, or grants recognizing exceptional academic or research performance.

Degree Requirement: Bachelor’s

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