Algorithm Developer (Quant Researcher) – 2027 PhDs

Hudson River Trading•New York, NY

3d•Hybrid

About The Position

Hudson River Trading (HRT) is seeking exceptional full-time PhD students to join our Algorithm Development teams in New York, London, and Singapore. Algorithm Developers at HRT are responsible for building and maintaining the models that drive our trading. A typical day involves applying rigorous statistical analysis to vast quantities of market and financial data to produce predictive trading models. In this role, you will work alongside fellow Algorithm Developers and Software Engineers to research, develop, and test novel order execution and model training methods to increase trading efficiency. This will involve running models live on our high-performance trading infrastructure and analyzing daily performance to maintain ongoing profitability. You can expect to apply your advanced academic research experience and expertise to impactful real world problems in trading across time horizons and machine learning strategies. Ideal candidates are excited to apply their research expertise to identify new opportunities in worldwide markets, enjoy both self-guided research and collaborating with others to analyze and fix problems efficiently, and are critical thinkers who can learn and implement new skills in a fast-changing environment.

Requirements

Full-time PhD student in a quantitative discipline (math, physics, computer science, statistics, or a related program) who is eligible for full-time roles in 2027.
Fluency in Python.
Experience with statistical analysis, numerical programming, or machine learning in Python, Pandas/Numpy, R, and/or MATLAB.
Brilliant analytical and problem-solving skills.
Ability to work creatively and independently on long-term technical problems.

Responsibilities

Building and maintaining the models that drive trading.
Applying rigorous statistical analysis to vast quantities of market and financial data to produce predictive trading models.
Researching, developing, and testing novel order execution and model training methods to increase trading efficiency.
Running models live on high-performance trading infrastructure.
Analyzing daily performance to maintain ongoing profitability.
Applying advanced academic research experience and expertise to impactful real-world problems in trading across time horizons and machine learning strategies.