About the role
The Role: This is a highly unique opportunity for a Data Scientist to join a new and rapidly growing intraday team. In this role you will partner with our close-knit team of quantitative researchers, data engineers, technologists and data sourcing colleagues to research, engineer, and validate quantitative signals derived from high-frequency equity market data across multiple global markets, while owning the full lifecycle of a signal, forming a hypothesis about what market behavior predicts, engineering it into a feature, validating it with data, and shipping it into a production research platform. This is a research-focused data science role with meaningful hands-on coding and implementing signals within our internal framework.
Key responsibilities include:
- Research and engineer features from raw, high-frequency market data, translating market behavior hypotheses into quantitative signals.
- Implement signals within our internal simulation/backtesting framework, iterating between exploratory data analysis and framework-based implementation.
- Validate features through backtesting across historical data, checking behavior across different markets, regimes, and edge cases (e.g., market open/close, low-liquidity periods).
- Collaborate with research and engineering teams to align on implementation approaches, validation standards, and research decisions.
- Explore new and existing data sources to identify candidate signals worth developing further.
- Becoming a domain expert on different deep learning and machine learning applications for high frequency data, analyzing & understanding the underlying dynamics, market microstructure and behaviors within the data.
- Develop insights based on the data and collaborate with the research team to generate tradable
- Developing the utility tools that can further automate the software development, testing and deployment workflow.
What You’ll Bring:
- Strong academic background – minimum of a bachelor’s degree in a technical or quantitative field.
- Strong data science background, with experience turning noisy, real-world data into validated, well-behaved signals or models.
- Rigorous quantitative programming skills, with the discipline to write accurate, production-quality, and performance aware code, including low-latency implementations where needed. Prior experience with C++ is a plus.
- Working knowledge of financial markets and how trading/market data behaves, or strong aptitude to learn it quickly. Prior market microstructure knowledge and experience with dark pools, trading and exchange data is a plus.
- Practical experience with and theoretical understanding of deep neural networks and other machine learning techniques in high frequency domain is a plus.
- Comfortably making pragmatic modeling tradeoffs under ambiguity while clearly articulating the reasoning behind them with data.
- Exceptional analytical & problem-solving abilities, with a strong attention to detail.