Quantitative Researcher

About Us

Qube Research & Technologies (QRT) is a global quantitative and systematic investment manager, operating in all liquid asset classes across the world. We are a technology and data driven group implementing a scientific approach to investing. Combining data, research, technology, and trading expertise has shaped QRT's collaborative mindset which enables us to solve the most complex challenges. QRT's culture of innovation continuously drives our ambition to deliver high quality returns for our investors.

The Role

Your core objective is to create high quality predictive signals. You will lead the full strategy research cycle from signal generation to implementation.

Responsibilities

• Create high quality predictive signals
• Leverage access to large and diversified datasets to identify statistical patterns and opportunities
• Share and discuss research results, methodology, data sets and processes with other researchers
• Implement the signals and the relevant datasets within the global execution platform
• Monitor signal behaviour and model performance over time
• Lead the full strategy research cycle from signal generation to implementation

Requirements

• Advanced degree in a quantitative field such as data science, statistics, mathematics, physics or engineering
• Coding skills required in at least one leading programming language (Python, R, Matlab and/or C++, C#)
• Capacity to multi-task in a fast paced environment while keeping strong attention to detail
• Intellectual curiosity to explore new data sets, solve complex problems, drive innovative processes and connect the dots between multiple fields
• Capacity to work with autonomy within a collegial and collaborative environment
• Strong capacity to communicate with technologists, data scientists and traders across the globe
• Proven track record in delivering successful systematic strategies

Nice to Have

• Strong knowledge in statistics, machine learning, NLP or AI techniques
• Experience in exploring large datasets across multiple time frames