Artificial Intelligence Researcher

Goliath Partners Inc.; New York, Northern (NY, KY); Hybrid; USD 400,000 - 2,000,000; Full time

Job summary

Goliath Partners Inc. in New York is seeking an ML researcher to push alpha research beyond classical statistical methods.

You will design, train, and deploy ML models for alpha generation across multiple liquid markets and own the end-to-end pipeline from research to live deployment. Ideal candidates have 2–5 years of ML experience in quantitative or high-stakes production settings, strong statistics background, and Python proficiency, with PhD or equivalent experience as a strong plus.

Qualifications

• PhD or 2 years of industry experience in ML, statistics, CS, or related quantitative field.
• Strong foundations in statistics and time-series methods; healthy skepticism about overfitting in noisy financial data.
• Proficiency in Python and large-scale data pipelines and model deployment infrastructure.

Responsibilities

• Design, train, and deploy ML models for alpha generation across equities, futures, and other liquid markets.
• Own the full pipeline from research and backtesting to live production deployment.
• Mine large, noisy, non-stationary financial datasets to engineer features and signals that hold up out-of-sample.
• Collaborate with quant researchers, engineers, and traders to integrate ML-driven signals into existing systematic strategies.

Skills

Python

Statistics

Time-series

NLP

Reinforcement learning

Deep learning

Ensemble methods

Education

PhD in ML / related field

Job description

Top-tier systematic trading firm hiring an ML researcher to push the firm's alpha research beyond classical statistical methods.

Compensation

• Base: $400K
• Total Comp: $1M-2M
• Structure: Base + Sign-on Bonus + P&L attachment + performance bonus

Role Description

• Design, train, and deploy ML models (deep learning, NLP, reinforcement learning, or large-scale ensemble methods) for alpha generation across equities, futures, and other liquid markets
• Own the full pipeline from research and backtesting to live production deployment
• Mine large, noisy, non-stationary financial datasets to engineer features and signals that hold up out-of-sample
• Collaborate with quant researchers, engineers, and traders to integrate ML-driven signals into existing systematic strategies

Ideal Candidate

• 2 to 5 years of experience applying machine learning in a quantitative research, trading, or comparable high-stakes production environment
• Strong foundations in statistics, applied ML, and time-series/panel data methods; healthy skepticism about overfitting in noisy financial data
• Proficiency in Python; experience with large-scale data pipelines and model deployment infrastructure
• PhD or 2 years of industry experience in ML, statistics, CS, or a related quantitative field a strong plus

Will wait up to 12 months non-compete.