The opportunity
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,…
What you'll do
Factor Mining & Validation: Discover, construct, and validate trading factors from multi-source data including market data, fundamental data, and 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 methods 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. Take ownership of 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 — improving 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 strategy operation in production.
Cross-Market AI Trading: Explore the adaptation and implementation of AI-driven trading across both traditional financial markets (equities, futures) and on-chain asset markets, leveraging the unique characteristics of each.
What they're looking for
- Master's degree or above in Computer Science, Mathematics, Statistics,: Financial Engineering, Physics, or related fields, with a 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 the ability to independently deliver an end-to-end strategy loop from data to live trading.
- Strong research capability and results-driven mindset, with the ability to: continuously optimize strategy performance in a fast-iteration environment.
