Agentic AI QR - Equities

Our client is a global leading hedge fund, who’s seeking a Quantitative Researcher to join a small, collaborative team focused on systematic equity strategies. This role offers the opportunity to contribute across the full research lifecycle, including idea generation, data sourcing, signal development, model implementation, backtesting, and live strategy refinement, with forecasting horizons spanning intraday to several weeks.

Responsibilities

• Partner closely with the SPM and team on alpha research for systematic equity strategies
• Generate and test new ideas using financial intuition, statistical learning, and large, diverse datasets
• Integrate Agentic AI workflows where they can improve productivity, model development, or operational robustness
• Source, clean, and analyze alternative, fundamental, and market microstructure data
• Build predictive models and contribute to signal combination, portfolio implementation, and ongoing model refinement
• Work in a transparent, collaborative environment with exposure to the broader investment process

Requirements

• Bachelor’s, Master’s, or PhD in a quantitative field such as Mathematics, Statistics, Computer Science, Physics, or a related STEM discipline
• Strong Python skills; experience building research tools or production-quality research infrastructure is highly desirable
• Minimum 3 years of experience in quantitative research focused on systematic equities, developing systematic equity or statistical arbitrage alphas, including intraday rebalancing of multi-day horizon signals
• Experience working with alternative, fundamental, and exchange / market microstructure data
• Practical experience applying LLMs or modern ML techniques to research workflows, signal generation, or dataset creation is highly desirable

Preferred Experience

• Experience combining heterogeneous signals across multiple data types and horizons
• Experience building custom or proprietary datasets
• Experience with sector-specific equity research
• Experience contributing to live trading strategy development in a small-team environment