Quantitative Researcher

Machine Learning Researcher | Systematic Equities

New York or Singapore

Join the equities quantitative research team at a global investment manager to develop machine learning models that forecast returns across global equities. You’ll work with engineers and traders to take your research from an initial hypothesis to a model used in live investment decisions.

The research

• Build sequence and time-series models to study price formation and forecast relative returns across stocks.
• Apply NLP and large language models to unstructured text, and test whether they add predictive information beyond what’s available in market data.
• Explore pre-training, fine-tuning, and reinforcement learning to identify signals that hold up as market conditions change.
• Work with engineers and traders to validate promising models, deploy them at scale, and evaluate their performance in production.

Your background

• An advanced degree in computer science, machine learning, mathematics, physics, engineering, or a related quantitative field.
• A track record of original ML research applied to real-world problems, backed by strong mathematical and statistical foundations.
• Deep expertise in an area such as sequence modeling, LLMs, or reinforcement learning, with hands-on experience training and optimizing models.
• Expert Python skills, fluency in PyTorch or JAX, and a genuine interest in how prices form and risk evolves in equity markets.

Message me for a confidential conversation about the team and its research in New York or Singapore.