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Binance is building an AI-driven trading system spanning traditional financial assets (equities, futures) and on-chain cryptocurrency assets. You will participate in the full lifecycle of quantitative strategy development—from factor discovery and validation through prediction modeling, strategy design, backtesting, and live deployment—combining quantitative research expertise with AI technology to generate sustainable alpha.
Key Responsibilities:
- Factor Mining & Validation: Discover, construct, and validate trading factors from multi-source data (market data, fundamental data, on-chain data). Continuously iterate the factor library to identify effective alpha signals.
- Factor Prediction Modeling: Design and optimize prediction models using machine learning and deep learning to improve signal accuracy and stability while controlling overfitting and strategy decay.
- Strategy Design & Backtesting: Lead the design, backtesting, and live deployment validation of trading strategies—covering signal generation, portfolio construction, risk control, and execution optimization. Own strategy P&L and risk performance.
- Quant Strategy Pipeline Development: Build and refine the end-to-end quantitative trading strategy pipeline from data ingestion, factor computation, model prediction, backtesting through to live execution. Improve research efficiency, deployability, and reproducibility.
- Trading System Integration: Collaborate with engineering and data teams to solve technical challenges including data connectivity, low-latency execution, and strategy deployment, ensuring stable production operation.
- Cross-Market AI Trading: Explore adaptation and implementation of AI-driven trading across traditional financial markets and on-chain asset markets, leveraging unique characteristics of each.
Requirements:
- Master's degree or above in Computer Science, Mathematics, Statistics, Financial Engineering, Physics, or related fields, with solid quantitative foundation and programming proficiency.
- Proven experience in quantitative trading strategy R&D, familiar with the full workflow of factor mining, factor prediction, strategy backtesting, and live deployment. Deep understanding of strategy P&L, risk, and alpha decay.
- Proficient in Python with hands-on experience applying ML/DL methods in quantitative scenarios and processing large-scale financial time-series data.
- Familiarity with trading mechanisms and data characteristics of at least one market (equities, futures, or other traditional financial markets; or cryptocurrency/on-chain assets). Understanding of real-world factors such as trading costs, liquidity, and execution slippage.
- Experience building a complete strategy pipeline or quantitative research platform, with ability to independently deliver an end-to-end strategy loop from data to live trading.
- Strong research capability and results-driven mindset, with ability to continuously optimize strategy performance in a fast-iteration environment.
Bonus Qualifications:
- Track record of managing capital at scale in live trading or generating sustained alpha.
- Cross-market quantitative experience spanning both traditional finance and on-chain markets (DeFi, CEX, DEX).
- Familiarity with high-frequency trading, market-making strategies, or cross-market arbitrage.
- Practical experience applying frontier AI methods (large language models, reinforcement learning) to trading strategies.
