🧠 Quantitative Machine Learning Researcher | New York / London

A leading global hedge fund with over $35B AUM is seeking a Quantitative Machine Learning Researcher to join its growing systematic research group. The team applies advanced ML and statistical techniques to develop predictive models, identify alpha signals, and optimize portfolio construction across global markets.

This is a unique opportunity for a top-tier researcher with strong academic credentials and hands-on technical skills to work in a world-class, data-driven environment with direct impact on live investment strategies.

Key Responsibilities:

Conduct research into machine learning and statistical methods to model market behaviour and generate alpha.
Design and test predictive features using large, diverse, and noisy datasets across equities, futures, and macro products.
Contribute to signal validation, model explainability, and robustness testing for production-ready strategies.
Collaborate with portfolio managers, engineers, and data scientists to integrate models into live trading frameworks.
Explore new data sources and ML techniques to expand signal coverage and performance.

Core Skills & Experience:

PhD (or Master’s with 1–2 years of experience) in a quantitative discipline such as Machine Learning, Computer Science, Statistics, Physics, Mathematics, or Engineering.
Degree from a top 20 global university (e.g., Oxford, Cambridge, MIT, Stanford, Harvard, Princeton, ETH, Imperial, etc.).
Strong background in machine learning, deep learning, or reinforcement learning.
Hands-on programming experience in Python (PyTorch, TensorFlow, NumPy, Pandas, Scikit-learn).
Solid understanding of statistical modeling, time-series analysis, and data preprocessing.
Familiarity with financial markets, quantitative trading, or asset pricing concepts preferred but not required.
Self-directed researcher with excellent problem-solving skills and a strong desire to work in a high-performance team.

💡 This is a rare opportunity to transition cutting-edge research into live trading impact — combining academic rigor with real-world scalability in a collaborative, world-class environment.

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