Quantitative Researcher

Goliath Partners; New York (NY); In loco; USD 180.000 - 240.000; Tempo pieno

Descrizione del lavoro

Goliath Partners Inc. in New York or Singapore is seeking a Machine Learning Researcher to join the Systematic Equities team.

You will develop ML models forecasting returns across global equities and collaborate with engineers and traders to move research from hypothesis to live models. You will work on sequence modeling, NLP with LLMs, and RL to identify signals that hold up in changing markets, with expert Python and PyTorch/JAX skills.

Competenze

• Advanced degree in a quantitative field such as CS, ML, math, physics, or engineering.
• Track record of original ML research applied to real problems with strong math and statistics foundations.
• Expert in sequence modeling, LLMs, or RL with hands-on training and optimization experience.

Mansioni

• Build sequence and time-series models to forecast returns across stocks.
• Apply NLP and large language models to unstructured text for predictive signals.
• Explore pre-training, fine-tuning, and reinforcement learning to identify signals as market conditions change.
• Collaborate with engineers and traders to deploy models at scale in production.

Conoscenze

Python

Sequence modeling

Reinforcement learning

ML research

Formazione

Advanced degree in computer science, machine learning, mathematics, physics, engineering, or related field

Strumenti

PyTorch

JAX

Descrizione del lavoro

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.