Machine Learning Researcher - PhD: 2027

Susquehanna International Group, LLP•New York, NY

1d•Onsite

About The Position

Susquehanna is expanding the Machine Learning group and seeking exceptional researchers to join our dynamic team. As a Machine Learning Researcher, you will apply advanced ML techniques to a wide range of forecasting challenges, including time series analysis, natural language understanding, and more. Your work will directly influence our trading strategies and decision-making processes. This is a unique opportunity to work at the intersection of cutting-edge research and real-world impact, leveraging one of the highest-quality financial datasets in the industry. We’re looking for research scientists with a proven track record of applying deep learning to solve complex, high-impact problems. The ideal candidate will have a strong grasp of diverse machine learning techniques and a passion for experimenting with model architectures, feature engineering, and hyperparameter tuning to produce resilient and high-performing models.

Requirements

PhD in computer science, machine learning, mathematics, physics, statistics, or a related field
Strong track record of applying ML in academic or industry settings, with 5+ years of experience building impactful deep learning systems
A strong publication record in top-tier conferences such as NeurIPS, ICML, or ICLR
Strong programming skills in Python and/or C++
Practical knowledge of ML libraries and frameworks, such as PyTorch or TensorFlow, especially in production environments
Hands-on experience applying deep learning on time series data
Strong foundation in mathematics, statistics, and algorithm design
Excellent problem-solving skills with a creative, research-driven mindset
Demonstrated ability to work collaboratively in team-oriented environments
A passion for solving complex problems and a drive to innovate in a fast-paced, competitive environment

Responsibilities

Research and develop deep learning models to generate and enhance systematic trading signals and strategies across asset classes.
Collaborate closely with researchers, traders, and developers to improve alpha generation and identify new algorithmic trading strategies.
Design and conduct rigorous experiments using modern machine learning frameworks to improve predictive signals and overall trading performance.
Apply scientific methods to extract actionable signals from complex datasets, deepening the understanding of market behavior.
Translate research insights into production-ready models that can be implemented, tested, and validated in live trading environments.
Partner with engineering and trading teams to deploy, monitor, and iterate on models that drive trading decisions and execution outcomes.