Artificial Intelligence Researcher

Goliath Partners; New York (NY); On-site; USD 1,000,000 - 2,000,000; Full time

Benefits offered by this job

Sign-on bonus

P&L attachment

Performance bonus

Job summary

Goliath Partners in New York seeks an ML researcher to push the firm's alpha research beyond classical statistical methods. You will design, train, and deploy ML models for alpha generation across equities, futures, and other liquid markets, owning the full pipeline from research to live production.

Ideal candidates have 2–5 years in ML for quantitative research or high-stakes production, strong statistics and time-series skills, and proficiency in Python with large-scale data pipelines.

Qualifications

• 2–5 years of experience applying ML in quantitative research, trading, or similar high-stakes production environments.
• Strong foundations in statistics and time-series methods; skepticism about overfitting in noisy data.

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 data to engineer robust features and signals for out-of-sample performance.
• Collaborate with quants, engineers, and traders to integrate ML-driven signals into strategies.

Skills

Python

Statistics

Time-series analysis

Deep learning

Education

PhD in ML/CS/Statistics

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.