Key Roles
Research
Quantitative Researcher
Work with other researchers on the full life cycle of the research process, including data processing, signal construction, backtesting and implementation. Conducting data-driven research, analyzing financial data, and creating quantitative models to generate insights and optimize trading strategies.
- Feature engineering research
- Machine learning, Deep Learning models research.
- Portfolio optimazation
- Alternative alpha research
- Order execution algorithms research
Trading
Executive Trader
Executes trades using various algorithms to minimize market impact and trading costs.
Quantitative Trader
Manage quantitative trading strategies by leveraging our state of the art trading technology & infrastructure.
Engineering
AI Platform, Machine Learning Engineer
Design, build and optimize state of the art research platform to improve overall scale, efficiency and usability. Design and implement cutting-edge machine learning training tools, ensuring a seamless transition from model backtesting to live trading. Streamline the workflow for researchers in managing trading models, data sets, and complex algorithms.
Data Engineering
Designing, building, and maintaining the systems, applications and infrastructure needed to collect, store, process, manage and query data assets. Ensuring that trustworthy data is available, reliable, and accessible to support various data-driven initiatives within Quant, Trading, Product and Compliance business units.
Quantitative Strategies Developer
Work directly with Traders and Researchers, focus on analysis, design, implementation, testing and delivery of quantitative trading strategies to the market. Develop software that incorporates vast amounts of data to drive sophisticated, ultra-fast strategies, capturing market opportunities before our competition.
Trading System Developer
Analysis, design, implementation, testing, and delivery of a trading system using the latest technologies. Apply performance optimization techniques across compilers, operating systems, and networking.
Infrastructure
- Kubernetes Developer: Design and development of the computational cluster infrastructure. Build machine cluster scheduling systems for CPUs and GPUs based on Kubernetes, allowing researchers to request resources for various machine learning training tasks at ease.
- DevOps: Deploy, monitor, and maintain Linux and Kubernetes platform systems. Develop and improve automated operational systems and tools, including updating related documentation.
- IT Support: Operation and maintenance of IT hardware, including computers, monitors, networks, switches, and rack servers.