AI Quantitative Researcher

Goliath Partners Inc.; New York (NY); On-site; USD 1,000,000 - 1,500,000; Full time

Job summary

Goliath Partners Inc. in New York is seeking an ML researcher to advance alpha research beyond classical methods, employing deep learning, NLP, reinforcement learning, and large-scale ensembles.

You will own the full lifecycle from research to production, work with quants and traders, and tackle noisy, non-stationary data to generate robust signals for diverse liquid markets.

Qualifications

• 2–5 years of ML experience in quantitative research or trading.
• Strong foundations in statistics and time-series methods.
• Proficiency in Python and/or C++ and production ML pipelines.
• PhD or 2 years of industry ML/quant experience is a strong plus.

Responsibilities

• Design, train, and deploy ML models for alpha generation across markets.
• Own the research-to-production pipeline.
• Engineer signals from large, noisy financial data.
• Collaborate with quants, engineers, and traders to integrate signals.

Skills

ML research

Time-series analysis

Python

C++

Model deployment

Education

PhD in ML / statistics / CS

Job description

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

Compensation

• Base: $350K
• Total Comp: $1M-1.5M
• 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 and/or C++; 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.