Full job description£200000 GBP

Discretionary Bonus

Onsite WORKING

Location: Paris, Central London, Central - United Kingdom Type: Permanent

PhD/Postdoc ML Quantitative Researcher

Role Overview:

My client is a leading global market-maker who are searching for exceptional ML Quantitative Researchers to join high-impact teams focused on systematic trading, predictive modelling, and machine learning research. These roles offer the opportunity to work in fast-paced, collaborative environments where research is directly connected to live PnL.

Teams vary in focus - from FX-driven research groups to multi-asset portfolio construction and optimization, but all are looking for individuals with Post-Doctoral Research, technical depth, and a passion for markets.

Key Responsibilities as a Quant Researcher: *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 as a Quant Researcher: *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, NLP, or pattern recognition techniques
*Experience with additional tools such as R, MATLAB, or ML 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