AI Quantitative Researcher

Goliath Partners; New York (NY); On-site; USD 350,000 - 1,500,000; Full time

Benefits offered by this job

Sign-on Bonus

P&L attachment

Performance bonus

Job summary

Goliath Partners 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 equities, futures, and other liquid markets.

You will own the full pipeline from research and backtesting to live production deployment, while mining large, noisy datasets to engineer robust signals and collaborating with quants, engineers, and traders to integrate ML-driven signals into existing strategies.

Qualifications

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

Responsibilities

• Design, train, and deploy ML models for alpha generation across markets.
• Own the full pipeline from research and backtesting to live deployment.
• Mine large, noisy financial data to engineer robust signals.
• Collaborate with quants, engineers, and traders to integrate ML signals.

Skills

ML

Time-series

Statistics

Data wrangling

Education

PhD in ML/Statistics/CS

Tools

Python

C++

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.