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Data Scientist - Trading insight

Binance
Singapore
Full time
Hybrid

Overview

Department

Data Science & Analytics

Job type

Full time

Compensation

Salary not specified

Location

Singapore

Company size

Start Up [ <10 employees ]

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Binance is seeking a Data Scientist to design and optimize quantitative trading strategies using statistical modeling, machine learning, and data-driven insights. The role involves analyzing large datasets and collaborating with developers to integrate models into trading systems.

Requirements

  • Bachelor’s, Master’s, or PhD in Computer Science, Applied Math, Statistics, or related fields.
  • 3+ years (mid-level) or 6+ years (senior) of experience in algorithmic or quantitative trading.
  • Solid background in Python (NumPy, pandas, scikit-learn, PyTorch) and/or C++ for performance-critical components.
  • Experience working with market microstructure data, including order books and tick-level data.
  • Strong foundation in probability theory, time-series modeling, and optimization.
  • Bonus: Experience with kdb+/q, columnar/time-series databases, or high-frequency trading systems. Bonus: Familiarity with crypto market structure, DEX/CEX dynamics, and derivatives trading.
  • Excellent communication and documentation skills; Self-driven, curious, and passionate about building scalable trading systems with impact.

    • Responsibilities

  • Strategy Development:Design and optimize quantitative trading strategies using statistical modeling, machine learning, and data-driven insights across digital asset markets.
  • Market Signal Research:Analyze large-scale datasets—including order book, tick-level, on-chain, and macroeconomic data—to identify predictive signals and alpha opportunities.
  • Backtesting & Simulation:Build and enhance robust backtesting frameworks to validate trading hypotheses under various market conditions and stress scenarios.
  • Execution Optimization:Collaborate with developers to integrate predictive models into low-latency trading systems and continuously improve execution performance.
  • Portfolio Analytics:Conduct post-trade analysis, PnL decomposition, and contribute to systematic portfolio optimization initiatives.
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