Junior Quantitative Researcher (Fresh STEM PhD graduates are welcome)

Responsibilities

• 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.

Requirements

• 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.

Nice to Have

• Prior internship or research experience at a hedge fund, prop trading firm, market maker, bank, or fintech.
• Exposure to market microstructure, limit order books, or high-frequency data.
• Experience with backtesting frameworks, time-series analysis, or causal inference.
• Familiarity with low-latency systems, or large-scale data infrastructure.
• Publications, open-source contributions, or trading competition results.