Quant Trade Researcher

Responsibilities

• Generate and test systematic trading hypotheses across spot, derivatives, and on-chain markets
• Build, validate, and maintain live alpha signals and execution models
• Run rigorous backtests, guarding against lookahead, data-leakage, and overfitting
• Analyse microstructure, order flow, and cross-venue dynamics to improve portfolio construction
• Collaborate with engineers to move research from notebook to production
• Monitor live strategy performance and iterate quickly on results
• Contribute to shared research infrastructure, tools, datasets, and code

Requirements

• Exceptional mathematical, statistical, or scientific pedigree (Olympiad medallists, PhDs, or top-decile graduates)
• Deep understanding of statistical learning, classical machine learning, and deep learning
• Strong experience implementing models including boosting algorithms, transformers, or reinforcement learning
• Strong programming ability, with Python required (C++ or Rust is a plus)
• Ability to own research end-to-end from first principles to production
• High agency and a relentless curiosity for solving unsolved problems

Preferred Qualifications

• For junior candidates: A strong research track record (papers, Kaggle wins, or competitive programming)
• For senior candidates: A live, attributable track record in systematic trading
• Experience or exposure to digital asset and crypto markets

About the Company

AlgoQuant Asset Management is a multi-strategy digital asset manager allocating capital across 25+ internal and external quantitative trading pods. Founded in 2018, we have evolved into an institutional platform combining trading edge with strong governance and advanced technology, serving family offices and institutional investors globally.