About this role
WorldQuant is seeking a Data Scientist to join a new and rapidly growing intraday team. This research-focused role involves partnering with quantitative researchers, data engineers, and technologists to research, engineer, and validate quantitative signals derived from high-frequency equity market data across global markets. You will own the full lifecycle of a signal, from forming hypotheses about market behavior to engineering features, validating them through backtesting, and deploying them into a production research platform. Responsibilities include: Researching and engineering features from raw high-frequency market data; Implementing signals within internal simulation and backtesting frameworks; Validating features across various market regimes and edge cases; Collaborating with cross-functional teams to align on implementation and research standards; Exploring new data sources; Applying deep learning and machine learning techniques to high-frequency data; Developing utility tools to automate development, testing, and deployment workflows. Requirements: A strong academic background with at least a bachelor’s degree in a technical or quantitative field; Proven experience in data science, specifically turning noisy, real-world data into validated models or signals; Rigorous quantitative programming skills with the ability to write production-quality, performance-aware code; Strong analytical and problem-solving abilities. Preferred qualifications include: Prior experience with C++; Knowledge of financial markets, market microstructure, dark pools, and trading data; Practical experience with deep neural networks and machine learning in high-frequency domains.