Junior Quantitative Researcher (Fresh STEM PhD graduates are welcome)

Binance is recognized as a prominent global blockchain ecosystem fundamental to the world's largest cryptocurrency exchange in terms of trading volume and user registration. Trusted by over 300 million individuals across 100+ countries, we are esteemed for our top-notch security measures, transparent user fund management, speedy trading engine, deep liquidity, and unparalleled range of digital asset products. Our comprehensive offerings extend from trading and financial services to education, research, payments, institutional support, Web3 functionalities, and more. We harness the potential of digital assets and blockchain technology to construct an all-encompassing financial ecosystem that promotes financial freedom and enhances financial accessibility worldwide.

We are in the process of establishing a new research unit that lies at the junction of artificial intelligence and quantitative trading to enhance the efficacy of execution algorithm models and beyond. Currently, we seek to fill the position of Junior Quantitative Researcher to play a leading role in this journey. The chosen candidate will collaborate with senior quant professionals, engineers, and traders to create AI-powered workflows aimed at producing alpha signals, diagnosing model and PnL behavior, and deepening our comprehension of market microstructure.

This role involves significant responsibility and is ideal for someone genuinely passionate about financial markets, equipped with a solid research background, and experienced in working with modern AI tools, such as LLM-based agents. While we are open to recent Ph.D. graduates, proficiency in demonstrating research depth and a genuine interest in trading is essential.

Responsibilities

• Conduct signal research and development by creating and testing predictive signals using statistical methods, machine learning, and AI agent-driven research workflows integrating various asset classes. See ideas from hypothesis stage through to deployment after backtesting and validation.
• Carry out root cause analysis (RCA) to explore model behaviors, signal deterioration, PnL attribution, and unexpected trading outcomes. Develop tools, including agentic ones, to expedite diagnosis processes and reduce the time between observation and resolution.
• Undertake market microstructure research to analyze order book dynamics, execution costs, liquidity, and venue behavior for refining signal design and execution strategies.
• Assist in designing and extending internal agentic systems to automate elements of the research process like data exploration, hypothesis generation, backtest setup, result summarization, and report drafting.
• Collaborate extensively with traders, engineers, and fellow researchers to transform concepts into live, monitored strategies.

Requirements

• Hold a Ph.D. degree (recently completed or nearing completion) in a quantitative discipline like Computer Science, Machine Learning, Statistics, Physics, Mathematics, Electrical Engineering, Operations Research, or related field.
• Possess strong Python programming skills and familiarity with the contemporary data and ML toolset (e.g., NumPy, pandas, PyTorch, JAX, etc.).
• Hands-on experience in building AI agents and LLM-based systems, demonstrating practical prowess in tools like multi-step reasoning pipelines, retrieval systems, tool-using agents, or evaluation frameworks.
• Proficient in statistics, probability, and machine learning, with the capacity to discern genuine results from spurious ones.
• Demonstrate a bona fide interest in financial markets and trading, as evidenced by coursework, personal projects, competition participation, internships, or self-initiated studies.
• Excellent written and verbal communication skills with the ability to convey technical work concisely to varied audiences.

Nice to Have

• Previous internship or research involvement with hedge funds, proprietary trading firms, market makers, banks, or fintech organizations.
• 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 extensive data infrastructure.
• Any publications, contributions to open-source projects, or achievements in trading competitions.