The opportunity
We are building out a new research function at the intersection of artificial intelligence and quantitative trading to improve the efficiency of execution algo models and more, and we are looking for a Junior Quantitative Researcher to be a founding member of this effort. You…
What you'll do
Signal research and construction. Develop, test, and productionize predictive: signals across asset classes using a combination of statistical methods, machine learning, and AI agent–driven research workflows. Take ideas from hypothesis through backtest, validation, and deployment.
Root cause analysis (RCA). Investigate model behavior, signal decay, PnL: attribution, and unexpected trading outcomes. Build tools — including agentic ones — that accelerate diagnosis and shorten the loop between observation and fix.
Market microstructure research. Study order book dynamics, execution costs,: liquidity, and venue behavior to inform both signal design and execution strategy.
AI agent infrastructure for research. Help design and extend internal agentic: systems that automate parts of the research pipeline — data exploration, hypothesis generation, backtest configuration, results summarization, and report drafting.
Collaborate broadly. Work closely with traders, engineers, and other: researchers to turn ideas into live, monitored strategies.
What they're looking for
- PhD (recently completed or near completion) in a quantitative field: e.g., Computer Science, Machine Learning, Statistics, Physics, Mathematics, Electrical Engineering, Operations Research, or a related discipline.
- Strong programming skills in Python; comfortable with the modern data and ML: stack (NumPy, pandas, PyTorch or JAX, etc.).
- Hands-on experience building with AI agents and LLM-based systems: for example, tool-using agents, multi-step reasoning pipelines, retrieval systems, or evaluation frameworks. We want to see that you have actually built things, not just read papers.
- Solid grounding in statistics, probability, and machine learning, with the: rigor to know when a result is real and when it isn't.
- Genuine interest in financial markets and trading, demonstrable through: coursework, personal projects, competitions, internships, or self-directed study.
- Strong written and verbal communication; able to explain technical work clearly to a mixed audience.
