Quantitative Researcher

OneBullEx is a crypto futures exchange built for speed, reliability, and innovation. Our platform delivers advanced systematic trading capabilities powered by AI and machine learning, enabling robust performance across highly volatile cryptocurrency and international financial markets. We are committed to transforming market complexity into structured, repeatable alpha through data-driven and fully automated trading strategies.

We are seeking a highly skilled Quantitative Researcher to join our elite systematic trading team and help design, research, and deploy next-generation trading strategies at scale.

Mission

Research, develop, and deploy AI-driven systematic trading strategies that identify market inefficiencies, generate sustainable alpha, and deliver strong risk-adjusted returns across crypto and global derivatives markets.

Key Responsibilities

• Strategy Research & Development
  
  Design, implement, and rigorously backtest high-frequency and mid-frequency systematic trading strategies across cryptocurrency and derivatives markets.
• Machine Learning & Deep Learning
  
  Research and apply advanced ML/DL techniques—including time series forecasting, decision-making models, and portfolio optimization—to extract predictive signals from noisy market data.
• Data Analysis & Signal Generation
  
  Process and analyze large-scale datasets, including tick-level market data, on-chain data, and alternative data sources, to uncover actionable trading signals.
• Financial Market Research
  
  Continuously study financial theory, market microstructure, and emerging research to enhance strategy robustness under volatile and stressed market conditions.
• Performance Monitoring & Optimization
  
  Monitor live trading performance, analyze execution quality, and iteratively refine strategies to adapt to changing market regimes.

Requirements

• Experience
  
  Minimum of 2 years of hands-on experience in quantitative research or systematic trading, preferably within cryptocurrency or international futures markets.
• Education
  
  Master's degree or Ph.D. in a quantitative discipline such as Computer Science, Financial Engineering, Mathematics, Physics, or Statistics, or equivalent exceptional industry experience.
• Technical Skills
• • Expert-level proficiency in Python for research and production environments
• Strong experience with Machine Learning and Deep Learning frameworks such as TensorFlow or PyTorch
• Solid understanding of financial time-series modeling and data-driven strategy design
• Quantitative Foundation
  
  Deep knowledge of statistics, probability theory, financial mathematics, and quantitative modeling.
• Communication
  
  Fluent in English with the ability to clearly explain complex quantitative concepts to both technical and non-technical stakeholders.

90-Day Objectives

• Deliver at least one production-ready trading strategy or research pipeline.
• Contribute meaningful improvements to existing strategy performance or robustness.
• Demonstrate strong ownership of research ideas from hypothesis to backtesting and evaluation.
• Integrate seamlessly with engineering and trading teams to support live deployment.

Why Join OneBullEx?

• Direct PnL Impact
  
  See your research deployed directly into live trading systems with measurable performance outcomes.
• Research-Driven Culture
  
  Work in a meritocratic environment where data, logic, and results drive decisions—not bureaucracy.
• Advanced Infrastructure
  
  Access cutting-edge computing resources, proprietary datasets, and a professional-grade trading platform.
• Elite Team
  
  Collaborate with experienced quantitative researchers, engineers, and traders operating at the forefront of digital asset markets.
• Competitive Package
  
  Industry-leading compensation with uncapped performance-based bonuses.

Next Steps

If you are a quantitative researcher passionate about systematic trading, machine learning, and real-world market impact, we would love to hear from you. All enquiries will be handled confidentially. Shortlisted candidates will be contacted to discuss next steps.