Quantitative ML Researcher: Forecasting & Trading Signals

SIG Susquehanna; New York (NY); On-site; USD 270,000 - 330,000; Full time

Job summary

Susquehanna is expanding its Machine Learning group in New York to hire a Machine Learning Researcher who will apply advanced ML to forecasting challenges, including time series and NLP, influencing trading strategies and decision-making.

You will design and run experiments, develop production-ready models, collaborate with researchers, traders, and engineers, and publish results. A PhD and 5+ years of deep learning experience and Python/C++ proficiency are preferred.

Qualifications

• PhD in computer science, machine learning, mathematics, physics, statistics, or related field.
• 5+ years of experience building impactful deep learning systems.
• Strong publication record in NeurIPS, ICML, or ICLR.
• Strong programming in Python and/or C++.
• Practical knowledge of ML libraries such as PyTorch or TensorFlow, especially in production environments.
• Hands-on experience applying deep learning on time series data.

Responsibilities

• Research and develop deep learning models to generate and enhance systematic trading signals.
• Collaborate with researchers, traders, and developers to improve alpha generation.
• Design and conduct experiments using modern ML frameworks to improve predictive signals.
• Extract actionable signals from complex datasets to understand market behavior.
• Translate research insights into production-ready models for live trading environments.
• Partner with engineering and trading teams to deploy, monitor, and iterate on models.

Skills

Python

C++

Deep learning

Time series

Experiment design

Collaboration

Education

PhD in CS/ML/related field

Tools

PyTorch

TensorFlow

Job description

Susquehanna is expanding its Machine Learning group in New York to hire a Machine Learning Researcher who will apply advanced ML to forecasting challenges, including time series and NLP, influencing trading strategies and decision-making.

You will design and run experiments, develop production-ready models, collaborate with researchers, traders, and engineers, and publish results. A PhD and 5+ years of deep learning experience and Python/C++ proficiency are preferred.