Junior Quantitative Researcher (Fresh STEM PhD graduates are welcome)

Binance is a leading global blockchain ecosystem behind the world’s largest cryptocurrency exchange by trading volume and registered users. We are trusted by 300+ million people in 100+ countries for our industry-leading security, user fund transparency, trading engine speed, deep liquidity, and an unmatched portfolio of digital-asset products. Binance offerings range from trading and finance to education, research, payments, institutional services, Web3 features, and more. We leverage the power of digital assets and blockchain to build an inclusive financial ecosystem to advance the freedom of money and improve financial access for people around the world.

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.