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.
Quantitative Researcher in New York City Metropolitan Area at Goliath Partners
This position is listed as full time and onsite. It was posted last week.