Senior Quant Researcher

AlgoQuant Asset ManagementDubai (preferred) London New York – Reports to Head of Research – Rolling startAbout AlgoQuantAlgoQuant Asset Management is a multi-strategy digital asset manager allocating capital across25+ internal and external quantitative trading pods. Founded in 2018, we have evolved into aninstitutional platform combining trading edge with strong governance and advanced technology,serving family offices and institutional investors globally.The roleWe are hiring a Senior Quant Researcher with deep machine learning and deep learning expertiseto drive the next generation of alpha research at AlgoQuant. This is a senior, high-ownership rolefor someone who has moved beyond applying ML frameworks — you understand why modelswork, where they break, and how to turn raw predictive signal into live, capital-weighted strategy.You will lead research into complex, non-linear signal generation across digital asset markets,working across spot, derivatives, and on-chain data. You will own research end-to-end: fromproblem formulation and data architecture through to live deployment and performance attribution.You will also set the standard for rigour and methodology across the research team.ResponsibilitiesDesign and deploy advanced ML and DL models for alpha signal generation across digitalasset marketsWork across the full model stack: feature engineering, architecture selection, training andvalidation regimes, and live signal monitoringApply and adapt state-of-the-art techniques — transformer architectures, graph neuralnetworks, reinforcement learning, and ensemble methods — to financial predictionproblemsBuild robust, production-grade research pipelines with a rigorous approach to preventinglookahead bias, data leakage, and overfittingAnalyse microstructure, order flow, and cross-venue dynamics to enrich feature sets andimprove signal qualityCollaborate with engineers to move models from research to production infrastructureMentor junior researchers and raise the bar for statistical rigour across the teamContribute to shared research infrastructure, tooling, and datasetsWhat we are looking forExceptional quantitative background — PhD or equivalent research depth in machinelearning, statistics, physics, mathematics, or computer scienceGenuine expertise in modern ML and DL: transformers, attention mechanisms, graphneural networks, boosting algorithms (XGBoost, LightGBM), and reinforcement learning —not just familiarity, but hands-on implementation experienceA track record of applying ML in a live, capital-at-risk environment — attributable P&L ormeasurable out-of-sample performance from systematic strategiesRigorous, almost paranoid approach to model validation — deeply experienced with thefailure modes of ML in finance: overfitting, regime change, feature leakage, andnon-stationarityStrong programming skills — Python required; C++ or Rust a strong plus for productionperformanceExperience working with large, complex, or unconventional datasets; on-chain dataexperience a plusSelf-directed and high-agency — you set your own research agenda and drive it tocompletionCrypto market exposure a strong plus; intellectual curiosity about digital asset marketstructure essential