Quantitative Trading Analyst: Data-Driven Markets

Find Data Science Jobs; Greater London; On-site; GBP 70,000 - 120,000; Full time

Job summary

DRW in London seeks a Quantitative Trading Analyst to join its data-driven trading desk. You will combine risk management, research and technology to create trading strategies and analyze market behavior.

You will work with traders, engineers and researchers across asset classes, gaining hands-on experience, handling large data sets, and translating insights into actionable tools in a fast-paced environment.

Qualifications

• 1-3 years of experience in quantitative research with a focus on machine learning, signal processing, optimization, or statistics.
• A bachelor’s, master’s, or PhD in mathematics, statistics, physics, engineering, computer science or related field
• Advanced quantitative, analytical and problem solving skills and the ability to deploy those skills during time-sensitive situations
• A deep curiosity of analyzing large data sets using your knowledge of probability and statistics
• Familiarity programming in Python, C++, C or similar languages

Responsibilities

• Investigate the potential application of existing strategies to new products
• Quantitatively analyze trade data and perform post-trade analysis of strategies
• Assist in the development of analytical tools
• Communicate relevant news, market events, and system behavior to team members in a clear and concise manner
• Be willing to challenge consensus on existing methodologies and trading strategies to contribute to the advancement of the team

Skills

Machine learning

Signal processing

Optimization

Statistics

Python

C++

Education

Bachelor's/Master's/PhD in mathematics or related field

Tools

Git

Jupyter

Job description

DRW in London seeks a Quantitative Trading Analyst to join its data-driven trading desk. You will combine risk management, research and technology to create trading strategies and analyze market behavior.

You will work with traders, engineers and researchers across asset classes, gaining hands-on experience, handling large data sets, and translating insights into actionable tools in a fast-paced environment.