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

£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