Quant Trade Researcher

Job description

## Quant Trade Researcher ***AlgoQuant Asset Management*** *Dubai (preferred) · London · New York – Reports to Head of Research – Rolling start* ### **About AlgoQuant** 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. ### **The role** We are hiring Quant Trade Researchers at both junior and senior levels to design, test, and deploy systematic trading strategies across digital asset markets. You will generate ideas from first principles, validate them with rigorous statistics, and ship signals into live capital, in weeks, not quarters. You will work directly with portfolio managers and engineers who move at your pace. This role is for people who live in data, mathematics, and code, relentlessly analytical, hungry for P&L, and happiest with a terminal and an unsolved problem in front of them. You will own research end-to-end, from first principles to production, and the work rewards raw firepower paired with the discipline to turn it into a signal. ### **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 ### **What we are looking for** - Exceptional mathematical, statistical, or scientific pedigree Olympiad medallists, PhDs, and top-decile graduates in maths, physics, computer science, or equivalent - Deep understanding of statistical learning, classical machine learning, and deep learning; strong experience implementing a wide range of models, including boosting algorithms, transformers, and reinforcement learning. - Strong programming ability, Python required, C++ or Rust a plus - Relentless curiosity and high agency, someone who cannot walk away from an unsolved problem - Comfort owning research end-to-end without hand-holding - For junior candidates: a research track record strong enough that we would hire on potential, papers, Kaggle wins, competitive programming results, or systematic trading experiments - For senior candidates: a live, attributable track record in systematic trading; crypto exposure a strong plus - A genuinely paranoid eye for data quality and bias, not comfortable until every result has a clear explanation - A genuine love for the subject, not just the paycheque