Job Description

Responsibilities

• Design, research, and validate  systematic alpha factors across price, order book, funding, flow, and microstructure data
• Build and maintain a  structured alpha research pipeline (data → feature → signal → evaluation → iteration)
• Conduct factor analysis including  IC, IR, decay, stability, regime sensitivity, and turnover analysis
• Collaborate with engineering teams to ensure  research outputs are production-ready
• Continuously iterate and improve existing alpha signals, even if historical performance has decayed
• Explore  AI-assisted research workflows for factor generation, feature selection, and hypothesis exploration (bonus)

Requirements

• 3+ years of quantitative research experience in systematic trading, alpha research, or related fields
• Strong proficiency in  Python, with hands-on experience using  Jupyter Notebook as a primary research environment
• Solid understanding of the  end-to-end alpha research process, including: Data cleaning & normalization, Feature engineering, Factor construction, Signal evaluation & validation.
• Have built and operated a  complete alpha research framework (personal or professional)
• Proven experience discovering alpha factors with  strong historical predictive power, e.g.: 1.  Information Coefficient (IC) consistently above  0.05–0.1 on daily frequency or higher IC on lower-frequency signals with reasonable stability (factors that later decayed are acceptable, as long as the original research process was sound)
• Strong analytical thinking and ability to explain  why a factor works, not just that it works

Nice to have

• Experience using  AI / ML models (e.g. tree models, neural networks, representation learning) for alpha research
• Hands-on experience with  local deployment of AI models (not just calling APIs)
• Familiarity with AI-assisted factor discovery workflows (feature generation, signal screening, regime detection, etc.)
• Background in crypto, derivatives, or high-frequency / microstructure-driven markets