onsite
Machine Learning Quantitative Researcher - Selby Jennings
ML Engineer
Join a high‑performing equity statistical arbitrage team as a Machine Learning Quantitative Researcher, developing and deploying data‑driven trading models using Python, ML techniques, and time‑series analysis.
About the role
Key Responsibilities
- Design, implement, and back‑test machine‑learning models for equity statistical arbitrage strategies.
- Analyze large, high‑frequency market data sets to extract predictive features and improve model performance.
- Collaborate closely with traders and engineers to integrate research outputs into live trading systems.
- Maintain and optimize data pipelines, ensuring data quality, latency, and scalability.
- Document research findings, present results to the pod, and contribute to the team’s knowledge base.
Requirements
- 0–4 years of experience in quantitative research, data science, or related fields.
- Strong programming skills in Python, with experience in libraries such as pandas, NumPy, scikit‑learn, or TensorFlow/PyTorch.
- Solid understanding of statistical arbitrage concepts, time‑series analysis, and financial market microstructure.
- Proficiency in SQL and experience handling large datasets in a Linux environment.
- Excellent problem‑solving abilities, strong communication skills, and a collaborative mindset.
Skills
pythonmachine learningsql