Data Scientist | Post Trade Analytics
Jump Trading is a global trading firm where traders, engineers, and researchers develop trading strategies, models, infrastructure, and systems across asset classes and time horizons.
About Jump Trading
Jump Trading is a global trading firm focused on research-driven trading and the engineering of scalable models, tools, infrastructure, and execution systems. Its operations combine trading, technology, AI/ML, and quantitative research, and it also runs research and talent programs including conference travel grants and a fellowship program.
Skills
Candidate Availability
Required and preferred rules are kept separate and reflect the wording in the original posting.
About the Role
You will analyze post-trade data to uncover competitive advantages and contribute to trading performance. You will identify relationships between business metrics and infrastructure changes, perform statistical analysis with trading teams, research exchange performance, quantify application impacts, design anomaly detection algorithms, and improve quantitative research frameworks using Python, C++, and related systems.
Requirements
- At least 3 years of Python programming experience focused on data science and analytics
- Ability to collaborate across trading and trading infrastructure teams
- Curiosity about high-frequency trading
- Ability to independently drive tasks and projects to completion
- Strong data intuition
- Excellent problem-solving abilities
- Experience documenting and communicating analysis to technical and business audiences
- Background in statistics
- Experience with C++
- Experience with SQL
- Practical experience with machine learning techniques
- Understanding of IP, UDP, TCP, and Ethernet protocols
- Reliable and predictable availability
Responsibilities
- Identify relationships between business metrics and infrastructure changes
- Perform post-trade statistical analysis on trading performance
- Research exchange performance
- Develop mathematical methodologies to quantify application impacts
- Design anomaly detection algorithms
- Develop quantitative research frameworks
- Improve quantitative research frameworks