ML Researcher: Systematic Equities & Live Deployment

Goliath Partners Inc.; New York (NY); Sur place; USD 160 000 - 230 000; Plein temps

Résumé du poste

Goliath Partners Inc. in New York is seeking a Machine Learning Researcher focused on systematic equities. You will join a collaborative team applying advanced ML to financial markets, with access to substantial data, compute, and engineering resources.

The role offers the opportunity to take research from experimentation through to live deployment. Responsibilities include developing models to forecast equity returns, applying DL, time-series modeling, NLP, and LLMs, and exploring pre-training,

Qualifications

• Advanced degree in CS, Math, Stats, Physics, Engineering, or related field.
• Strong track record of original research with practical applications.
• Deep expertise in machine learning, with experience in sequence modeling, LLMs, reinforcement learning, or related areas.
• Strong Python skills and experience with PyTorch or JAX.

Responsabilités

• Develop models to forecast equity returns and identify patterns in market behavior.
• Apply deep learning, time-series modeling, NLP, and LLMs to structured and unstructured data.
• Explore pre-training, fine-tuning, and reinforcement learning to extract signals from noisy, changing markets.
• Work closely with researchers, engineers, and traders to deploy scalable, robust models.

Connaissances

Python

Deep learning

Research experience

Formation

Advanced degree (MS/PhD) in CS/Math/Stats/Physics/Engineering

Outils

PyTorch

JAX

Description du poste

Goliath Partners Inc. in New York is seeking a Machine Learning Researcher focused on systematic equities. You will join a collaborative team applying advanced ML to financial markets, with access to substantial data, compute, and engineering resources.

The role offers the opportunity to take research from experimentation through to live deployment. Responsibilities include developing models to forecast equity returns, applying DL, time-series modeling, NLP, and LLMs, and exploring pre-training,